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[] | Poster | [] | Simulations of turbulent flows in 3D are one of the most expensive simulations in computational fluid dynamics (CFD). Many works have been written on surrogate models to replace numerical solvers for fluid flows with faster, learned, autoregressive models. However, the intricacies of turbulence in three dimensions necessitate training these models with very small time steps, while generating realistic flow states requires either long roll-outs with many steps and significant error accumulation or starting from a known, realistic flow state—something we aimed to avoid in the first place. Instead, we propose to approach turbulent flow simulation as a generative task directly learning the manifold of all possible turbulent flow states without relying on any initial flow state. For our experiments, we introduce a challenging 3D turbulence dataset of high-resolution flows and detailed vortex structures caused by various objects and derive two novel sample evaluation metrics for turbulent flows. On this dataset, we show that our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples amenable for downstream applications without access to any initial state. | [] | [] | From Zero to Turbulence: Generative Modeling for 3D Flow Simulation | [
"Marten Lienen",
"David Lüdke",
"Jan Hansen-Palmus",
"Stephan Günnemann"
] | 2306.01776 | 18,386 | https://openreview.net/forum?id=ZhlwoC1XaN |
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[] | Poster | [] | Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models.In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset.In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks. | [] | [] | Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets | [
"Dominique Beaini",
"Shenyang Huang",
"Joao Alex Cunha",
"Zhiyi Li",
"Gabriela Moisescu-Pareja",
"Oleksandr Dymov",
"Samuel Maddrell-Mander",
"Callum McLean",
"Frederik Wenkel",
"Luis Müller",
"Jama Hussein Mohamud",
"Ali Parviz",
"Michael Craig",
"Michał Koziarski",
"Jiarui Lu",
"Zhaocheng Zhu",
"Cristian Gabellini",
"Kerstin Klaser",
"Josef Dean",
"Cas Wognum",
"Maciej Sypetkowski",
"Guillaume Rabusseau",
"Reihaneh Rabbany",
"Jian Tang",
"Christopher Morris",
"Mirco Ravanelli",
"Guy Wolf",
"Prudencio Tossou",
"Hadrien Mary",
"Therence Bois",
"Andrew William Fitzgibbon",
"Blazej Banaszewski",
"Chad Martin",
"Dominic Masters"
] | 2310.04292 | 18,388 | https://openreview.net/forum?id=Zc2aIcucwc |
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[] | Poster | [] | Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs. Previous efforts in detecting hallucinations have been employed at logit-level uncertainty estimation or language-level self-consistency evaluation, where the semantic information is inevitably lost during the token-decoding procedure. Thus, we propose to explore the dense semantic information retained within LLMs' \textbf{IN}ternal \textbf{S}tates for halluc\textbf{I}nation \textbf{DE}tection (\textbf{INSIDE}). In particular, a simple yet effective \textbf{EigenScore} metric is proposed to better evaluate responses' self-consistency, which exploits the eigenvalues of responses' covariance matrix to measure the semantic consistency/diversity in the dense embedding space. Furthermore, from the perspective of self-consistent hallucination detection, a test time feature clipping approach is explored to truncate extreme activations in the internal states, which reduces overconfident generations and potentially benefits the detection of overconfident hallucinations. Extensive experiments and ablation studies are performed on several popular LLMs and question-answering (QA) benchmarks, showing the effectiveness of our proposal. | [] | [] | INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection | [
"Chao Chen",
"Kai Liu",
"Ze Chen",
"Yi Gu",
"Yue Wu",
"Mingyuan Tao",
"Zhihang Fu",
"Jieping Ye"
] | 2402.03744 | 18,385 | https://openreview.net/forum?id=Zj12nzlQbz |
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[] | Spotlight Poster | [] | Disentangled representation learning remains challenging as the underlying factors of variation in the data do not naturally exist. The inherent complexity of real-world data makes it unfeasible to exhaustively enumerate and encapsulate all its variations within a finite set of factors. However, it is worth noting that most real-world data have linguistic equivalents, typically in the form of textual descriptions. These linguistic counterparts can represent the data and effortlessly decomposed into distinct tokens. In light of this, we present Vocabulary Disentangled Retrieval (VDR), a retrieval-based framework that harnesses natural language as proxies of the underlying data variation to drive disentangled representation learning. Our approach employ a bi-encoder model to represent both data and natural language in a vocabulary space, enabling the model to distinguish dimensions that capture intrinsic characteristics within data through its natural language counterpart, thus facilitating disentanglement. We extensively assess the performance of VDR across 15 retrieval benchmark datasets, covering text-to-text and cross-modal retrieval scenarios, as well as human evaluation. Our experimental results compellingly demonstrate the superiority of VDR over previous bi-encoder retrievers with comparable model size and training costs, achieving an impressive 8.7% improvement in NDCG@10 on the BEIR benchmark, a 5.3\% increase on MS COCO, and a 6.0% increase on Flickr30k in terms of mean recall in the zero-shot setting. Moreover, The results from human evaluation indicate that interpretability of our method is on par with SOTA captioning models. | [] | [] | Retrieval-based Disentangled Representation Learning with Natural Language Supervision | [
"Jiawei Zhou",
"Xiaoguang Li",
"Lifeng Shang",
"Xin Jiang",
"Qun Liu",
"Lei Chen"
] | 2212.07699 | 18,384 | https://openreview.net/forum?id=ZlQRiFmq7Y |
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[] | Poster | [
"https://github.com/dyballa/dynamic-connectomes"
] | The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior. | [] | [] | Learning dynamic representations of the functional connectome in neurobiological networks | [
"Luciano Dyballa",
"Samuel Lang",
"Alexandra Haslund-Gourley",
"Eviatar Yemini",
"Steven W. Zucker"
] | 2402.14102 | 18,382 | https://openreview.net/forum?id=ZwhHSOHMTM |
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[] | Poster | [] | Graph contrastive learning (GCL) has become a powerful tool for learning graph data, but its scalability remains a significant challenge. In this work, we propose a simple yet effective training framework called Structural Compression (StructComp) to address this issue. Inspired by a sparse low-rank approximation on the diffusion matrix, StructComp trains the encoder with the compressed nodes. This allows the encoder not to perform any message passing during the training stage, and significantly reduces the number of sample pairs in the contrastive loss. We theoretically prove that the original GCL loss can be approximated with the contrastive loss computed by StructComp. Moreover, StructComp can be regarded as an additional regularization term for GCL models, resulting in a more robust encoder. Empirical studies on seven benchmark datasets show that StructComp greatly reduces the time and memory consumption while improving model performance compared to the vanilla GCL models and scalable training methods. | [] | [] | StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning | [
"Shengzhong Zhang",
"Wenjie Yang",
"Xinyuan Cao",
"Hongwei Zhang",
"Zengfeng Huang"
] | 2312.04865 | 18,378 | https://openreview.net/forum?id=a4DBEeGfQq |
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[] | Poster | [] | Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performance in many settings. This success presents a seemingly surprising observation: these methods suffer relatively little from overfitting, i.e., when a prompt is manually engineered to achieve low error on a given training set (thus rendering the method no longer actually zero-shot), the approach still performs well on held-out test data. In this paper, we show that we can explain such performance well via recourse to classical PAC-Bayes bounds. Specifically, we show that the discrete nature of prompts, combined with a PAC-Bayes prior given by a language model, results in generalization bounds that are remarkably tight by the standards of the literature: for instance, the generalization bound of an ImageNet classifier is often within a few percentage points of the true test error. We demonstrate empirically that this holds for existing handcrafted prompts and prompts generated through simple greedy search. Furthermore, the resulting bound is well-suited for model selection: the models with the best bound typically also have the best test performance. This work thus provides a possible justification for the widespread practice of "prompt engineering," even if it seems that such methods could potentially overfit the training data. | [] | [] | Understanding prompt engineering may not require rethinking generalization | [
"Victor Akinwande",
"Yiding Jiang",
"Dylan Sam",
"J Zico Kolter"
] | 2310.03957 | 18,377 | https://openreview.net/forum?id=a745RnSFLT |
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[] | Poster | [] | Object-centric architectures usually apply a differentiable module to the entire feature map to decompose it into sets of entity representations called slots. Some of these methods structurally resemble clustering algorithms, where the cluster's center in latent space serves as a slot representation. Slot Attention is an example of such a method, acting as a learnable analog of the soft k-means algorithm. Our work employs a learnable clustering method based on the Gaussian Mixture Model. Unlike other approaches, we represent slots not only as centers of clusters but also incorporate information about the distance between clusters and assigned vectors, leading to more expressive slot representations. Our experiments demonstrate that using this approach instead of Slot Attention improves performance in object-centric scenarios, achieving state-of-the-art results in the set property prediction task. | [] | [] | Object-Centric Learning with Slot Mixture Module | [
"Daniil Kirilenko",
"Vitaliy Vorobyov",
"Alexey Kovalev",
"Aleksandr Panov"
] | 2311.04640 | 18,374 | https://openreview.net/forum?id=aBUidW4Nkd |
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[] | Poster | [] | We study risk-sensitive Reinforcement Learning (RL), where we aim to maximizethe Conditional Value at Risk (CVaR) with a fixed risk tolerance $\tau$. Prior theoretical work studying risk-sensitive RL focuses on the tabular Markov Decision Processes (MDPs) setting. To extend CVaR RL to settings where state space is large, function approximation must be deployed. We study CVaR RL in low-rank MDPs with nonlinear function approximation. Low-rank MDPs assume the underlying transition kernel admits a low-rank decomposition, but unlike prior linear models, low-rank MDPs do not assume the feature or state-action representation is known. We propose a novel Upper Confidence Bound (UCB) bonus-driven algorithm to carefully balance the interplay between exploration, exploitation, and representation learning in CVaR RL. We prove that our algorithm achieves a sample complexity of $\tilde{O}\left(\frac{H^7 A^2 d^4}{\tau^2 \epsilon^2}\right)$ to yield an $\epsilon$-optimal CVaR, where $H$ is the length of each episode, $A$ is the capacity of action space, and $d$ is the dimension of representations.Computational-wise, we design a novel discretized Least-Squares Value Iteration (LSVI) algorithm for the CVaR objective as the planning oracle and show that we can find the near-optimal policy in a polynomial running time with a Maximum Likelihood Estimation oracle. To our knowledge, this is the first provably efficient CVaR RL algorithm in low-rank MDPs. | [] | [] | Provably Efficient CVaR RL in Low-rank MDPs | [
"Yulai Zhao",
"Wenhao Zhan",
"Xiaoyan Hu",
"Ho-fung Leung",
"Farzan Farnia",
"Wen Sun",
"Jason D. Lee"
] | 2311.11965 | 19,279 | https://openreview.net/forum?id=9x6yrFAPnx |
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[] | Poster | [] | With the continuous advancement of neural network methodologies, time series prediction has attracted substantial interest over the past decades. Nonetheless, the interpretability of neural networks is insufficient and the utilization of deep learning techniques for prediction necessitates significant computational expenditures, rendering its application arduous in numerous scenarios. In order to tackle this challenge, an interpretable sparse system identification method which does not require a time-consuming training through back-propagation is proposed in this study. This method integrates advantages from both knowledge-based and data-driven approaches, and constructs dictionary functions by leveraging Fourier basis and taking into account both the long-term trends and the short-term fluctuations behind data. By using the $l_1$ norm for sparse optimization, prediction results can be gained with an explicit sparse expression function and an extremely high accuracy. The performance evaluation of the proposed method is conducted on comprehensive benchmark datasets, including ETT, Exchange, and ILI. Results reveal that our proposed method attains a significant overall improvement of more than 20\% in accordance with the most recent state-of-the-art deep learning methodologies. Additionally, our method demonstrates the efficient training capability on only CPUs. Therefore, this study may shed some light onto the realm of time series reconstruction and prediction. | [] | [] | Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction | [
"Xiaoyi Liu",
"Duxin Chen",
"Wenjia Wei",
"Xia Zhu",
"Wenwu Yu"
] | 18,373 | https://openreview.net/forum?id=aFWUY3E7ws |
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[] | Poster | [] | Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience. While latent variable models (LVMs) are successful in describing high-dimensional data, they are typically only designed for a single type of data, making it difficult to identify structure shared across different experimental data modalities. Here, we address this shortcoming by proposing an unsupervised LVM which extracts shared and independent latents for distinct, simultaneously recorded experimental modalities. We do this by combining Gaussian Process Factor Analysis (GPFA), an interpretable LVM for neural spiking data with temporally smooth latent space, with Gaussian Process Variational Autoencoders (GP-VAEs), which similarly use a GP prior to characterize correlations in a latent space, but admit rich expressivity due to a deep neural network mapping to observations. We achieve interpretability in our model by partitioning latent variability into components that are either shared between or independent to each modality. We parameterize the latents of our model in the Fourier domain, and show improved latent identification using this approach over standard GP-VAE methods. We validate our model on simulated multi-modal data consisting of Poisson spike counts and MNIST images that scale and rotate smoothly over time. We show that the multi-modal GP-VAE (MM-GPVAE) is able to not only identify the shared and independent latent structure across modalities accurately, but provides good reconstructions of both images and neural rates on held-out trials. Finally, we demonstrate our framework on two real world multi-modal experimental settings: Drosophila whole-brain calcium imaging alongside tracked limb positions, and Manduca sexta spike train measurements from ten wing muscles as the animal tracks a visual stimulus. | [] | [] | Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data | [
"Rabia Gondur",
"Usama Bin Sikandar",
"Evan Schaffer",
"Mikio Christian Aoi",
"Stephen L Keeley"
] | 2310.03111 | 18,372 | https://openreview.net/forum?id=aGH43rjoe4 |
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[] | Poster | [] | Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy. Despite its clear importance, there has been little work that explains how fine-tuning alters the underlying capabilities learnt by a model during pretraining: does fine-tuning yield entirely novel capabilities or does it just inhibit existing ones? An answer to this question would improve our ability to trust fine-tuning protocols meant to improve the safety of pre-trained models and delete unsafe capabilities. We aim to make progress on this question by answering it in controlled settings where we can use mechanistic interpretability tools (e.g.~ network pruning and probing) to understand how the model's underlying capabilities are changing. We perform an exhaustive analysis of the effects of fine-tuning in these settings, and show: (i) the ubiquitous protocol of fine-tuning with a small learning rate rarely alters the underlying model capabilities; (ii) often a minimal transformation, which we call a wrapper, is learned on top of the underlying model capability, yielding the impression that a new capability has been learned or a prior capability has been deleted; and (iii) continuing the fine-tuning process on a task where the pretraining capabilities are relevant leads to sample-efficient ``revival'' of the capability, i.e., the model starts to accurately reuse that capability in just a few gradient steps. \textit{This potentially indicates a practitioner could unintentionally render a safe model to be unsafe by merely fine-tuning on a downstream task.} We additionally perform analysis on language models trained on the TinyStories dataset to support our claims in a realistic setting. | [] | [] | Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks | [
"Samyak Jain",
"Robert Kirk",
"Ekdeep Singh Lubana",
"Robert P. Dick",
"Hidenori Tanaka",
"Tim Rocktäschel",
"Edward Grefenstette",
"David Krueger"
] | 2311.12786 | 19,277 | https://openreview.net/forum?id=A0HKeKl4Nl |
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[] | Poster | [
"https://github.com/bbbbby-99/InsertNeRF"
] | Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce **InsertNeRF**, a method for **INS**tilling g**E**ne**R**alizabili**T**y into **NeRF**. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings. | [] | [] | InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules | [
"Yanqi Bao",
"Tianyu Ding",
"Jing Huo",
"Wenbin Li",
"Yuxin Li",
"Yang Gao"
] | 2308.13897 | 18,371 | https://openreview.net/forum?id=aHmNpLlUlb |
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[] | Poster | [] | Integrating a notion of symmetry into point cloud neural networks is a provably effective way to improve their generalization capability. Of particular interest are $E(3)$ equivariant point cloud networks where Euclidean transformations applied to the inputs are preserved in the outputs. Recent efforts aim to extend networks that are equivariant with respect to a single global $E(3)$ transformation, to accommodate inputs made of multiple parts, each of which exhibits local $E(3)$ symmetry.In practical settings, however, the partitioning into individually transforming regions is unknown a priori.Errors in the partition prediction would unavoidably map to errors in respecting the true input symmetry. Past works have proposed different ways to predict the partition, which may exhibit uncontrolled errors in their ability to maintain equivariance to the actual partition. To this end, we introduce APEN: a general framework for constructing approximate piecewise-$E(3)$ equivariant point networks. Our framework offers an adaptable design to guaranteed bounds on the resulting piecewise $E(3)$ equivariance approximation errors.Our primary insight is that functions which are equivariant with respect to a finer partition (compared to the unknown true partition) will also maintain equivariance in relation to the true partition. Leveraging this observation, we propose a compositional design for a partition prediction model. It initiates with a fine partition and incrementally transitions towards a coarser subpartition of the true one, consistently maintaining piecewise equivariance in relation to the current partition.As a result, the equivariance approximation error can be bounded solely in terms of (i) uncertainty quantification of the partition prediction, and (ii) bounds on the probability of failing to suggest a proper subpartition of the ground truth one.We demonstrate the practical effectiveness of APEN using two data types exemplifying part-based symmetry: (i) real-world scans of room scenes containing multiple furniture-type objects; and, (ii) human motions, characterized by articulated parts exhibiting rigid movement. Our empirical results demonstrate the advantage of integrating piecewise $E(3)$ symmetry into network design, showing a distinct improvement in generalization over prior works in terms of generalization accuracy for both classification and segmentation tasks. | [] | [] | Approximately Piecewise E(3) Equivariant Point Networks | [
"Matan Atzmon",
"Francis Williams",
"Jiahui Huang",
"Or Litany"
] | 2402.08529 | 18,369 | https://openreview.net/forum?id=aKJEHWmBEf |
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[] | Poster | [] | Recent studies have shown that the regret of reinforcement learning (RL) can be polylogarithmic in the planning horizon $H$. However, it remains an open question whether such a result holds for adversarial RL. In this paper, we answer this question affirmatively by proposing the first horizon-free policy search algorithm. To tackle the challenges caused by exploration and adversarially chosen reward over episodes, our algorithm employs (1) a variance-uncertainty-aware weighted least square estimator for the transition kernel; and (2) an occupancy measure-based technique for the online search of a stochastic policy. We show that our algorithm achieves an $\tilde{O}\big((d+\log |\mathcal{S}|)\sqrt{K} + d^2\big)$ regret with full-information feedback, where $d$ is the dimension of a known feature mapping linearly parametrizing the unknown transition kernel of the MDP, $K$ is the number of episodes, $|\mathcal{S}|$ is the cardinality of the state space. We also provide hardness results to justify the near optimality of our algorithm and the inevitability of $\log|\mathcal{S}|$ in the regret bound. | [] | [] | Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs | [
"Kaixuan Ji",
"Qingyue Zhao",
"Jiafan He",
"Weitong Zhang",
"Quanquan Gu"
] | 2305.08359 | 18,366 | https://openreview.net/forum?id=aPNwsJgnZJ |
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[] | Poster | [
"https://github.com/nicehuster/SymPoint"
] | This work studies the problem of panoptic symbol spotting, which is to spot and parse both countable object instances (windows, doors, tables, etc.) and uncountable stuff (wall, railing, etc.) from computer-aided design (CAD) drawings. Existing methods typically involve either rasterizing the vector graphics into images and using image-based methods for symbol spotting, or directly building graphs and using graph neural networks for symbol recognition. In this paper, we take a different approach, which treats graphic primitives as a set of 2D points that are locally connected and use point cloud segmentation methods to tackle it. Specifically, we utilize a point transformer to extract the primitive features and append a mask2former-like spotting head to predict the final output. To better use the local connection information of primitives and enhance their discriminability, we further propose the attention with connection module (ACM) and contrastive connection learning scheme (CCL). Finally, we propose a KNN interpolation mechanism for the mask attention module of the spotting head to better handle primitive mask downsampling, which is primitive-level in contrast to pixel-level for the image. Our approach, named SymPoint, is simple yet effective, outperforming recent state-of-the-art method GAT-CADNet by an absolute increase of 9.6% PQ and 10.4% RQ on the FloorPlanCAD dataset. The source code and models will be available at \url{https://github.com/nicehuster/SymPoint}. | [] | [] | Symbol as Points: Panoptic Symbol Spotting via Point-based Representation | [
"WENLONG LIU",
"Tianyu Yang",
"Yuhan Wang",
"Qizhi Yu",
"Lei Zhang"
] | 2401.10556 | 18,367 | https://openreview.net/forum?id=aOnUe8ah7j |
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[] | Poster | [] | We study the problem of procedure planning in instructional videos, which aims to make a goal-oriented sequence of action steps given partial visual state observations. The motivation of this problem is to learn a structured and plannable state and action space. Recent works succeeded in sequence modeling of steps with only sequence-level annotations accessible during training, which overlooked the roles of states in the procedures. In this work, we point out that State CHangEs MAtter (SCHEMA) for procedure planning in instructional videos. We aim to establish a more structured state space by investigating the causal relations between steps and states in procedures. Specifically, we explicitly represent each step as state changes and track the state changes in procedures. For step representation, we leveraged the commonsense knowledge in large language models (LLMs) to describe the state changes of steps via our designed chain-of-thought prompting. For state changes tracking, we align visual state observations with language state descriptions via cross-modal contrastive learning, and explicitly model the intermediate states of the procedure using LLM-generated state descriptions. Experiments on CrossTask, COIN, and NIV benchmark datasets demonstrate that our proposed SCHEMA model achieves state-of-the-art performance and obtains explainable visualizations. | [] | [] | SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos | [
"Yulei Niu",
"Wenliang Guo",
"Long Chen",
"Xudong Lin",
"Shih-Fu Chang"
] | 2403.01599 | 18,363 | https://openreview.net/forum?id=abL5LJNZ49 |
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[] | Spotlight Poster | [] | The recent success of Transformer in natural language processing has sparked its use in various domains. In offline reinforcement learning (RL), Decision Transformer (DT) is emerging as a promising model based on Transformer. However, we discovered that the attention module of DT is not appropriate to capture the inherent local dependence pattern in trajectories of RL modeled as a Markov decision process. To overcome the limitations of DT, we propose a novel action sequence predictor, named Decision ConvFormer (DC), based on the architecture of MetaFormer, which is a general structure to process multiple entities in parallel and understand the interrelationship among the multiple entities. DC employs local convolution filtering as the token mixer and can effectively capture the inherent local associations of the RL dataset. In extensive experiments, DC achieved state-of-the-art performance across various standard RL benchmarks while requiring fewer resources. Furthermore, we show that DC better understands the underlying meaning in data and exhibits enhanced generalization capability. | [] | [] | Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making | [
"Jeonghye Kim",
"Suyoung Lee",
"Woojun Kim",
"Youngchul Sung"
] | 2310.03022 | 18,361 | https://openreview.net/forum?id=af2c8EaKl8 |
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[] | Poster | [] | Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On the flip side, these models have numerous drawbacks, including their potential to generate images featuring sexually explicit content, mirror artistic styles without permission, or even hallucinate (or deepfake) the likenesses of celebrities. Consequently, various methods have been proposed in order to "erase" sensitive concepts from text-to-image models. In this work, we examine seven recently proposed concept erasure methods, and show that targeted concepts are not fully excised from any of these methods. Specifically, we leverage the existence of special learned word embeddings that can retrieve "erased" concepts from the sanitized models with no alterations to their weights. Our results highlight the brittleness of post hoc concept erasure methods, and call into question their use in the algorithmic toolkit for AI safety. | [] | [] | Circumventing Concept Erasure Methods For Text-To-Image Generative Models | [
"Minh Pham",
"Kelly O. Marshall",
"Niv Cohen",
"Govind Mittal",
"Chinmay Hegde"
] | 2308.01508 | 18,359 | https://openreview.net/forum?id=ag3o2T51Ht |
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[] | Poster | [] | We present shuffle coding, a general method for optimal compression of sequences of unordered objects using bits-back coding. Data structures that can be compressed using shuffle coding include multisets, graphs, hypergraphs, and others. We release an implementation that can easily be adapted to different data types and statistical models, and demonstrate that our implementation achieves state-of-the-art compression rates on a range of graph datasets including molecular data. | [] | [] | Entropy Coding of Unordered Data Structures | [
"Julius Kunze",
"Daniel Severo",
"Giulio Zani",
"Jan-Willem van de Meent",
"James Townsend"
] | 18,360 | https://openreview.net/forum?id=afQuNt3Ruh |
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[] | Poster | [] | According to the efficient coding hypothesis, neural populations encode information optimally when representations are high-dimensional and uncorrelated. However, such codes may carry a cost in terms of generalization and robustness. Past empirical studies of early visual cortex (V1) in rodents have suggested that this tradeoff indeed constrains sensory representations. However, it remains unclear whether these insights generalize across the hierarchy of the human visual system, and particularly to object representations in high-level occipitotemporal cortex (OTC). To gain new empirical clarity, here we develop a family of object recognition models with parametrically varying dropout proportion $p$, which induces systematically varying dimensionality of internal responses (while controlling all other inductive biases). We find that increasing dropout produces an increasingly smooth, low-dimensional representational space. Optimal robustness to lesioning is observed at around 70% dropout, after which both accuracy and robustness decline. Representational comparison to large-scale 7T fMRI data from occipitotemporal cortex in the Natural Scenes Dataset reveals that this optimal degree of dropout is also associated with maximal emergent neural predictivity. Finally, using new techniques for achieving denoised estimates of the eigenspectrum of human fMRI responses, we compare the rate of eigenspectrum decay between model and brain feature spaces. We observe that the match between model and brain representations is associated with a common balance between efficiency and robustness in the representational space. These results suggest that varying dropout may reveal an optimal point of balance between the efficiency of high-dimensional codes and the robustness of low dimensional codes in hierarchical vision systems. | [] | [] | Manipulating dropout reveals an optimal balance of efficiency and robustness in biological and machine visual systems | [
"Jacob S. Prince",
"Gabriel Fajardo",
"George A. Alvarez",
"Talia Konkle"
] | 19,272 | https://openreview.net/forum?id=ADDCErFzev |
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[] | Poster | [] | Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task planners becomes difficult, and there exists a dearth of detailed exploration regarding the effects of various factors such as pre-trained model selection and prompt construction. To address this, we propose a benchmark system for automatically quantifying performance of task planning for home-service embodied agents. Task planners are tested on two pairs of datasets and simulators: 1) ALFRED and AI2-THOR, 2) an extension of Watch-And-Help and VirtualHome. Using the proposed benchmark system, we perform extensive experiments with LLMs and prompts, and explore several enhancements of the baseline planner. We expect that the proposed benchmark tool would accelerate the development of language-oriented task planners. | [] | [] | LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents | [
"Jae-Woo Choi",
"Youngwoo Yoon",
"Hyobin Ong",
"Jaehong Kim",
"Minsu Jang"
] | 19,271 | https://openreview.net/forum?id=ADSxCpCu9s |
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[] | Poster | [] | Speech quality estimation has recently undergone a paradigm shift from human-hearing expert designs to machine-learning models. However, current models rely mainly on supervised learning, which is time-consuming and expensive for label collection. To solve this problem, we propose VQScore, a self-supervised metric for evaluating speech based on the quantization error of a vector-quantized-variational autoencoder (VQ-VAE). The training of VQ-VAE relies on clean speech; hence, large quantization errors can be expected when the speech is distorted. To further improve correlation with real quality scores, domain knowledge of speech processing is incorporated into the model design. We found that the vector quantization mechanism could also be used for self-supervised speech enhancement (SE) model training. To improve the robustness of the encoder for SE, a novel self-distillation mechanism combined with adversarial training is introduced. In summary, the proposed speech quality estimation method and enhancement models require only clean speech for training without any label requirements. Experimental results show that the proposed VQScore and enhancement model are competitive with supervised baselines. The code will be released after publication. | [] | [] | Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech | [
"Szu-Wei Fu",
"Kuo-Hsuan Hung",
"Yu Tsao",
"Yu-Chiang Frank Wang"
] | 2402.16321 | 18,357 | https://openreview.net/forum?id=ale56Ya59q |
|
[] | Spotlight Poster | [] | The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. However, due to the heterogeneity among tables, such DNN bonus is still far from being well exploited on tabular data prediction (e.g., regression or classification tasks). Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables. In this paper, we present TP-BERTa, a specifically pre-trained LM model for tabular data prediction. Concretely, a novel relative magnitude tokenization converts scalar numerical feature values to finely discrete, high-dimensional tokens, and an intra-feature attention approach integrates feature values with the corresponding feature names. Comprehensive experiments demonstrate that our pre-trained TP-BERTa leads the performance among tabular DNNs and is competitive with Gradient Boosted Decision Tree models in typical tabular data regime. Our pre-trained model will be publicly available. | [] | [] | Making Pre-trained Language Models Great on Tabular Prediction | [
"Jiahuan Yan",
"Bo Zheng",
"Hongxia Xu",
"Yiheng Zhu",
"Danny Chen",
"Jimeng Sun",
"Jian Wu",
"Jintai Chen"
] | 2403.01841 | 18,356 | https://openreview.net/forum?id=anzIzGZuLi |
|
[] | Poster | [
"https://github.com/YuWVandy/Topo_LP_GNN"
] | Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP). While numerous studies aim to improve the overall LP performance of GNNs, none have explored its varying performance across different nodes and its underlying reasons. To this end, we aim to demystify which nodes will perform better from the perspective of their local topology. Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors. We empirically demonstrate that TC has a higher correlation with LP performance than other node-level topological metrics like degree and subgraph density, offering a better way to identify low-performing nodes than using cold-start. With TC, we discover a novel topological distribution shift issue in which newly joined neighbors of a node tend to become less interactive with that node's existing neighbors, compromising the generalizability of node embeddings for LP at testing time. To make the computation of TC scalable, We further propose Approximated Topological Concentration (ATC) and theoretically/empirically justify its efficacy in approximating TC and reducing the computation complexity. Given the positive correlation between node TC and its LP performance, we explore the potential of boosting LP performance via enhancing TC by re-weighting edges in the message-passing and discuss its effectiveness with limitations. Our code is publicly available at https://github.com/submission2024/Topo_Concentration | [] | [] | A Topological Perspective on Demystifying GNN-Based Link Prediction Performance | [
"Yu Wang",
"Tong Zhao",
"Yuying Zhao",
"Yunchao Liu",
"Xueqi Cheng",
"Neil Shah",
"Tyler Derr"
] | 2310.04612 | 18,354 | https://openreview.net/forum?id=apA6SSXx2e |
|
[] | Poster | [] | Prompts play a crucial role in guiding the responses of Large Language Models (LLMs). However, the intricate role of individual tokens in prompts, known as input saliency, in shaping the responses remains largely underexplored. Existing saliency methods either misalign with LLM generation objectives or rely heavily on linearity assumptions, leading to potential inaccuracies. To address this, we propose Token Distribution Dynamics (TDD), an elegantly simple yet remarkably effective approach to unveil and manipulate the role of prompts in generating LLM outputs. TDD leverages the robust interpreting capabilities of the language model head (LM head) to assess input saliency. It projects input tokens into the embedding space and then estimates their significance based on distribution dynamics over the vocabulary. We introduce three TDD variants: forward, backward, and bidirectional, each offering unique insights into token relevance. Extensive experiments reveal that the TDD surpasses state-of-the-art baselines with a big margin in elucidating the causal relationships between prompts and LLM outputs. Beyond mere interpretation, we apply TDD to two prompt manipulation tasks for controlled text generation: zero-shot toxic language suppression and sentiment steering. Empirical results underscore TDD's proficiency in identifying both toxic and sentimental cues in prompts, subsequently mitigating toxicity or modulating sentiment in the generated content. | [] | [] | Unveiling and Manipulating Prompt Influence in Large Language Models | [
"Zijian Feng",
"Hanzhang Zhou",
"ZIXIAO ZHU",
"Junlang Qian",
"Kezhi Mao"
] | 18,355 | https://openreview.net/forum?id=ap1ByuwQrX |
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[] | Poster | [] | Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of value networks. Existing studies focus on unboundedly maximizing this rank; nevertheless, that approach would introduce overly complex models in the learning, thus undermining performance. Hence, fine-tuning representation rank presents a challenging and crucial optimization problem. To address this issue, we find a guiding principle for adaptive control of the representation rank. We employ the Bellman equation as a theoretical foundation and derive an upper bound on the cosine similarity of consecutive state-action pairs representations of value networks. We then leverage this upper bound to propose a novel regularizer, namely BEllman Equation-based automatic rank Regularizer (BEER). This regularizer adaptively regularizes the representation rank, thus improving the DRL agent's performance. We first validate the effectiveness of automatic control of rank on illustrative experiments. Then, we scale up BEER to complex continuous control tasks by combining it with the deterministic policy gradient method. Among 12 challenging DeepMind control tasks, BEER outperforms the baselines by a large margin. Besides, BEER demonstrates significant advantages in Q-value approximation. Our anonymous code is available at https://anonymous.4open.science/r/BEER-3C4B. | [] | [] | Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation | [
"Qiang He",
"Tianyi Zhou",
"Meng Fang",
"Setareh Maghsudi"
] | 2404.12754 | 18,353 | https://openreview.net/forum?id=apXtolxDaJ |
|
[] | Spotlight Poster | [] | By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.However, how receptive are LLMs to such external evidence, especially when the evidence conflicts with their parametric memory? We present the first comprehensive and controlled investigation into the behavior of LLMs when encountering knowledge conflicts.We propose a systematic framework to elicit high-quality parametric memory from LLMs and construct the corresponding counter-memory, which enables us to conduct a series of controlled experiments.Our investigation reveals seemingly contradicting behaviors of LLMs.On the one hand, different from prior wisdom, we find that LLMs can be highly receptive to external evidence even when that conflicts with their parametric memory, given that the external evidence is coherent and convincing.On the other hand, LLMs also demonstrate a strong confirmation bias when the external evidence contains some information that is consistent with their parametric memory, despite being presented with conflicting evidence at the same time.These results pose important implications that are worth careful consideration for the further development and deployment of tool- and retrieval-augmented LLMs.Resources are available at https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict. | [] | [] | Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts | [
"Jian Xie",
"Kai Zhang",
"Jiangjie Chen",
"Renze Lou",
"Yu Su"
] | 2305.13300 | 18,352 | https://openreview.net/forum?id=auKAUJZMO6 |
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[] | Poster | [] | Bayesian filtering approximates the true underlying behavior of a time-varying system by inverting an explicit generative model to convert noisy measurements into state estimates. This process typically requires matrix storage, inversion, and multiplication or Monte Carlo estimation, none of which are practical in high-dimensional state spaces such as the weight spaces of artificial neural networks. Here, we consider the standard Bayesian filtering problem as optimization over a time-varying objective. Instead of maintaining matrices for the filtering equations or simulating particles, we specify an optimizer that defines the Bayesian filter implicitly. In the linear-Gaussian setting, we show that every Kalman filter has an equivalent formulation using K steps of gradient descent. In the nonlinear setting, our experiments demonstrate that our framework results in filters that are effective, robust, and scalable to high-dimensional systems, comparing well against the standard toolbox of Bayesian filtering solutions. We suggest that it is easier to fine-tune an optimizer than it is to specify the correct filtering equations, making our framework an attractive option for high-dimensional filtering problems. | [] | [] | Implicit Maximum a Posteriori Filtering via Adaptive Optimization | [
"Gianluca Bencomo",
"Jake Snell",
"Thomas L. Griffiths"
] | 2311.10580 | 18,351 | https://openreview.net/forum?id=auUngos7eR |
|
[] | Poster | [] | Convex relaxations are a key component of training and certifying provably safe neural networks. However, despite substantial progress, a wide and poorly understood accuracy gap to standard networks remains, raising the question of whether this is due to fundamental limitations of convex relaxations. Initial work investigating this question focused on the simple and widely used IBP relaxation. It revealed that some univariate, convex, continuous piecewise linear (CPWL) functions cannot be encoded by any ReLU network such that its IBP-analysis is precise.To explore whether this limitation is shared by more advanced convex relaxations, we conduct the first in-depth study on the expressive power of ReLU networks across all commonly used convex relaxations. We show that: (i) more advanced relaxations allow a larger class of univariate functions to be expressed as precisely analyzable ReLU networks, (ii) more precise relaxations can allow exponentially larger solution spaces of ReLU networks encoding the same functions, and (iii) even using the most precise single-neuron relaxations, it is impossible to construct precisely analyzable ReLU networks that express multivariate, convex, monotone CPWL functions. | [] | [] | Expressivity of ReLU-Networks under Convex Relaxations | [
"Maximilian Baader",
"Mark Niklas Mueller",
"Yuhao Mao",
"Martin Vechev"
] | 2311.04015 | 18,350 | https://openreview.net/forum?id=awHTL3Hpto |
|
[] | Poster | [] | Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual prompt. However, the internal representations learned by these models remain an enigma. In this work, we present Conceptor, a novel method to interpret the internal representation of a textual concept by a diffusion model. This interpretation is obtained by decomposing the concept into a small set of human-interpretable textual elements. Applied over the state-of-the-art Stable Diffusion model, Conceptor reveals non-trivial structures in the representations of concepts. For example, we find surprising visual connections between concepts, that transcend their textual semantics. We additionally discover concepts that rely on mixtures of exemplars, biases, renowned artistic styles, or a simultaneous fusion of multiple meanings of the concept.Through a large battery of experiments, we demonstrate Conceptor's ability to provide meaningful, robust, and faithful decompositions for a wide variety of abstract, concrete, and complex textual concepts, while allowing to naturally connect each decomposition element to its corresponding visual impact on the generated images. | [] | [] | The Hidden Language of Diffusion Models | [
"Hila Chefer",
"Oran Lang",
"Mor Geva",
"Volodymyr Polosukhin",
"Assaf Shocher",
"michal Irani",
"Inbar Mosseri",
"Lior Wolf"
] | 2306.00966 | 18,349 | https://openreview.net/forum?id=awWpHnEJDw |
|
[] | Poster | [] | Relying on prior knowledge accumulated from related tasks, meta-learning offers a powerful approach to learning a novel task from a limited number of training data. Recent approaches use a family of prior probability density functions or recurrent neural network models, whose parameters can be optimized by utilizing labeled data from the observed tasks. While these approaches have appealing empirical performance, expressiveness of their prior is relatively low, which limits generalization and interpretation of meta-learning. Aiming at expressive yet meaningful priors, this contribution puts forth a novel prior representation model that leverages the notion of algorithm unrolling. The key idea is to unroll the proximal gradient descent steps, where learnable piecewise linear functions are developed to approximate the desired proximal operators within *tight* theoretical error bounds established for both smooth and non-smooth proximal functions. The resultant multi-block neural network not only broadens the scope of learnable priors, but also enhances interpretability from an optimization viewpoint. Numerical tests conducted on few-shot learning datasets demonstrate markedly improved performance with flexible, visualizable, and understandable priors. | [] | [] | Meta-Learning Priors Using Unrolled Proximal Networks | [
"Yilang Zhang",
"Georgios B. Giannakis"
] | 18,347 | https://openreview.net/forum?id=b3Cu426njo |
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[] | Poster | [] | Deep long-tailed recognition (DTLR) has attracted much attention due to its close touch with realistic scenarios. Recent advances have focused on re-balancing across various aspects, e.g., sampling strategy, loss re-weighting, logit adjustment, and input/parameter perturbation, to name a few. However, few studies have considered dynamic re-balancing to address intrinsic optimization conflicts. In this paper, we first empirically argue that the optimizations of mainstream DLTR methods are still dominated by some categories (e.g., major) due to a fixed re-balancing strategy. Thus, they fail to deal with gradient conflicts among categories, which naturally deduces the motivation for reaching Pareto optimal solutions. Unfortunately, a naive integration of multi-objective optimization (MOO) with DLTR methods is not applicable due to the gap between multi-task learning (MTL) and DLTR, and can in turn lead to class-specific feature degradation. Thus, we provide effective alternatives by decoupling MOO-based MTL from the temporal rather than structure perspective, and enhancing it via optimizing variability collapse loss motivated by the derived MOO-based DLTR generalization bound. Moreover, we resort to anticipating worst-case optimization with theoretical insights to further ensure convergence. We build a Pareto deep long-tailed recognition method termed PLOT upon the proposed MOO framework. Extensive evaluations demonstrate that our method not only generally improves mainstream pipelines, but also achieves an augmented version to realize state-of-the-art performance across multiple benchmarks. | [] | [] | Pareto Deep Long-Tailed Recognition: A Conflict-Averse Solution | [
"Zhipeng Zhou",
"Liu Liu",
"Peilin Zhao",
"Wei Gong"
] | 18,344 | https://openreview.net/forum?id=b66P1u0k15 |
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[] | Poster | [] | Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded their use in real-world language applications. However, the critical challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. While previous in-context learning research has focused on enhancing models to adhere to users' specific instructions and quality expectations, and to avoid undesired outputs, little to no work has explored the use of task-specific fine-tuned Language Models (SLMs) to improve LLMs' in-context learning during the inference stage. Our primary contribution is the establishment of a simple yet effective framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks. Using our proposed plug-in method, enhanced versions of Llama 2 and ChatGPT surpass their original versions regarding generalizability and factuality. We offer a comprehensive suite of resources, including 16 curated datasets, prompts, model checkpoints, and LLM outputs across 9 distinct tasks. Our empirical analysis sheds light on the advantages of incorporating discriminative models into LLMs and highlights the potential of our methodology in fostering more reliable LLMs. | [] | [] | Supervised Knowledge Makes Large Language Models Better In-context Learners | [
"Linyi Yang",
"Shuibai Zhang",
"Zhuohao Yu",
"Guangsheng Bao",
"Yidong Wang",
"Jindong Wang",
"Ruochen Xu",
"Wei Ye",
"Xing Xie",
"Weizhu Chen",
"Yue Zhang"
] | 2312.15918 | 18,343 | https://openreview.net/forum?id=bAMPOUF227 |
|
[] | Poster | [
"https://github.com/TXH-mercury/COSA"
] | Due to the limited scale and quality of video-text training corpus, most vision-language foundation models employ image-text datasets for pretraining and primarily focus on modeling visually semantic representations while disregarding temporal semantic representations and correlations. To address this issue, we propose COSA, a COncatenated SAmple pretrained vision-language foundation model. COSA can jointly model visual contents and event-level temporal cues using only image-text corpora. We achieve this by sequentially concatenating multiple image-text pairs as inputs for pretraining. This transformation effectively converts existing image-text corpora into a pseudo video-paragraph corpus, enabling richer scene transformations and explicit event-description correspondence. Extensive experiments demonstrate that COSA consistently improves performance across a broad range of semantic vision-language downstream tasks, including paragraph-to-video retrieval, text-to-video/image retrieval, video/image captioning and video QA. Notably, COSA achieves state-of-the-art results on various competitive benchmarks. Models and codes will be released. | [] | [] | COSA: Concatenated Sample Pretrained Vision-Language Foundation Model | [
"Sihan Chen",
"Xingjian He",
"Handong Li",
"Xiaojie Jin",
"Jiashi Feng",
"Jing Liu"
] | 2306.09085 | 18,341 | https://openreview.net/forum?id=bDkisS75zy |
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[] | Poster | [] | This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs). Our work provides a novel perspective by examining in-context learning via the lens of surface repetitions. We quantitatively investigate the role of surface features in text generation, and empirically establish the existence of token co-occurrence reinforcement, a principle that strengthens the relationship between two tokens based on their contextual co-occurrences. By investigating the dual impacts of these features, our research illuminates the internal workings of in-context learning and expounds on the reasons for its failures. This paper provides an essential contribution to the understanding of in-context learning and its potential limitations, providing a fresh perspective on this exciting capability. | [] | [] | Understanding In-Context Learning from Repetitions | [
"Jianhao Yan",
"Jin Xu",
"Chiyu Song",
"Chenming Wu",
"Yafu Li",
"Yue Zhang"
] | 2310.00297 | 18,340 | https://openreview.net/forum?id=bGGYcvw8mp |
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[] | Poster | [] | Markov processes are widely used mathematical models for describing dynamic systems in various fields. However, accurately simulating large-scale systems at long time scales is computationally expensive due to the short time steps required for accurate integration. In this paper, we introduce an inference process that maps complex systems into a simplified representational space and models large jumps in time. To achieve this, we propose Time-lagged Information Bottleneck (T-IB), a principled objective rooted in information theory, which aims to capture relevant temporal features while discarding high-frequency information to simplify the simulation task and minimize the inference error. Our experiments demonstrate that T-IB learns information-optimal representations for accurately modeling the statistical properties and dynamics of the original process at a selected time lag, outperforming existing time-lagged dimensionality reduction methods. | [] | [] | Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck | [
"Marco Federici",
"Patrick Forré",
"Ryota Tomioka",
"Bastiaan S. Veeling"
] | 2309.07200 | 18,339 | https://openreview.net/forum?id=bH6T0Jjw5y |
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[] | Poster | [] | This paper considers the problem of learning the reward function and constraints of an expert from few demonstrations. This problem can be considered as a meta-learning problem where we first learn meta-priors over reward functions and constraints from other distinct but related tasks and then adapt the learned meta-priors to new tasks from only few expert demonstrations. We formulate a bi-level optimization problem where the upper level aims to learn a meta-prior over reward functions and the lower level is to learn a meta-prior over constraints. We propose a novel algorithm to solve this problem and formally guarantee that the algorithm reaches the set of $\epsilon$-stationary points at the iteration complexity $O(\frac{1}{\epsilon^2})$. We also quantify the generalization error to an arbitrary new task. Experiments are used to validate that the learned meta-priors can adapt to new tasks with good performance from only few demonstrations. | [] | [] | Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis | [
"Shicheng Liu",
"Minghui Zhu"
] | 18,338 | https://openreview.net/forum?id=bJ3gFiwRgi |
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[] | Poster | [] | As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to traditional full-finetuning. However, the conditions favoring VPT (the "when") and the underlying rationale (the "why") remain unclear. In this paper, we conduct a comprehensive analysis across 19 distinct datasets and tasks. To understand the "when" aspect, we identify the scenarios where VPT proves favorable by two dimensions: task objectives and data distributions. We find that VPT is preferrable when there is 1) a substantial disparity between the original and the downstream task objectives ($e.g.$, transitioning from classification to counting), or 2) a notable similarity in data distributions between the two tasks ($e.g.$, both involve natural images). In exploring the "why" dimension, our results indicate VPT's success cannot be attributed solely to overfitting and optimization considerations. The unique way VPT preserves original features and adds parameters appears to be a pivotal factor. Our study provides insights into VPT's mechanisms, and offers guidance for its optimal utilization. | [] | [] | Facing the Elephant in the Room: Visual Prompt Tuning or Full finetuning? | [
"Cheng Han",
"Qifan Wang",
"Yiming Cui",
"Wenguan Wang",
"Lifu Huang",
"Siyuan Qi",
"Dongfang Liu"
] | 2401.12902 | 18,337 | https://openreview.net/forum?id=bJx4iOIOxn |
|
[] | Poster | [] | Video activity localization aims at understanding the semantic content in long, untrimmed videos and retrieving actions of interest. The retrieved action with its start and end locations can be used for highlight generation, temporal action detection, etc. Unfortunately, learning the exact boundary location of activities is highly challenging because temporal activities are continuous in time, and there are often no clear-cut transitions between actions. Moreover, the definition of the start and end of events is subjective, which may confuse the model. To alleviate the boundary ambiguity, we propose to study the video activity localization problem from a denoising perspective. Specifically, we propose an encoder-decoder model named DenosieLoc. During training, a set of action spans is randomly generated from the ground truth with a controlled noise scale. Then, we attempt to reverse this process by boundary denoising, allowing the localizer to predict activities with precise boundaries and resulting in faster convergence speed. Experiments show that DenosieLoc advances several video activity understanding tasks. For example, we observe a gain of +12.36% average mAP on the QV-Highlights dataset.Moreover, DenosieLoc achieves state-of-the-art performance on the MAD dataset but with much fewer predictions than others. | [] | [] | Boundary Denoising for Video Activity Localization | [
"Mengmeng Xu",
"Mattia Soldan",
"Jialin Gao",
"Shuming Liu",
"Juan-Manuel Perez-Rua",
"Bernard Ghanem"
] | 2304.02934 | 18,335 | https://openreview.net/forum?id=bLpUtGyf9g |
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[] | Poster | [] | Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities. Despite the fast development of Neural radiance field (NeRF) techniques and their wide applications in both indoor and outdoor scenes, applying NeRF to multi-camera systems remains very challenging. This is primarily due to the inherent under-calibration issues in multi-camera setup, including inconsistent imaging effects stemming from separately calibrated image signal processing units in diverse cameras, and system errors arising from mechanical vibrations during driving that affect relative camera poses.In this paper, we present UC-NeRF, a novel method tailored for novel view synthesis in under-calibrated multi-view camera systems.Firstly, we propose a layer-based color correction to rectify the color inconsistency in different image regions. Second, we propose virtual warping to generate more viewpoint-diverse but color-consistent virtual views for color correction and 3D recovery. Finally, a spatiotemporally constrained pose refinement is designed for more robust and accurate pose calibration in multi-camera systems.Our method not only achieves state-of-the-art performance of novel view synthesis in multi-camera setups, but also effectively facilitates depth estimation in large-scale outdoor scenes with the synthesized novel views. | [] | [] | UC-NERF: Neural Radiance Field for Under-Calibrated Multi-View Cameras in Autonomous Driving | [
"Kai Cheng",
"Xiaoxiao Long",
"Wei Yin",
"Jin Wang",
"Zhiqiang Wu",
"Yuexin Ma",
"Kaixuan Wang",
"Xiaozhi Chen",
"Xuejin Chen"
] | 18,336 | https://openreview.net/forum?id=bLKcCe7hYh |
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[] | Poster | [] | We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves. | [] | [] | Raidar: geneRative AI Detection viA Rewriting | [
"Chengzhi Mao",
"Carl Vondrick",
"Hao Wang",
"Junfeng Yang"
] | 2401.12970 | 18,333 | https://openreview.net/forum?id=bQWE2UqXmf |
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[] | Poster | [] | Training deep neural networks (DNNs) is computationally expensive, which is problematic especially when performing duplicated or similar training runs in model ensemble or fine-tuning pre-trained models, for example. Once we have trained one DNN on some dataset, we have its learning trajectory (i.e., a sequence of intermediate parameters during training) which may potentially contain useful information for learning the dataset. However, there has been no attempt to utilize such information of a given learning trajectory for another training. In this paper, we formulate the problem of "transferring" a given learning trajectory from one initial parameter to another one (named *learning transfer problem*) and derive the first algorithm to approximately solve it by matching gradients successively along the trajectory via permutation symmetry. We empirically show that the transferred parameters achieve non-trivial accuracy before any direct training, and can be trained significantly faster than training from scratch. | [] | [] | Transferring Learning Trajectories of Neural Networks | [
"Daiki Chijiwa"
] | 2305.14122 | 18,330 | https://openreview.net/forum?id=bWNJFD1l8M |
|
[] | Poster | [
"https://github.com/imoneoi/openchat"
] | Nowadays, open-source large language models like LLaMA have emerged. Recent developments have incorporated supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT) to align these models with human goals. However, SFT methods treat all training data with mixed quality equally, while RLFT methods require high-quality pairwise or ranking-based preference data. In this study, we present a novel framework, named OpenChat, to advance open-source language models with mixed-quality data. Specifically, we consider the general SFT training data, consisting of a small amount of expert data mixed with a large proportion of sub-optimal data, without any preference labels. We propose the C(onditioned)-RLFT, which regards different data sources as coarse-grained reward labels and learns a class-conditioned policy to leverage complementary data quality information. Interestingly, the optimal policy in C-RLFT can be easily solved through single-stage, RL-free supervised learning, which is lightweight and avoids costly human preference labeling. Through extensive experiments on three standard benchmarks, our openchat-13b fine-tuned with C-RLFT achieves the highest average performance among all 13b open-source language models. Moreover, we use AGIEval to validate the model generalization performance, in which only openchat-13b surpasses the base model. Finally, we conduct a series of analyses to shed light on the effectiveness and robustness of OpenChat. Our code, data, and models will be publicly available. | [] | [] | OpenChat: Advancing Open-source Language Models with Mixed-Quality Data | [
"Guan Wang",
"Sijie Cheng",
"Xianyuan Zhan",
"Xiangang Li",
"Sen Song",
"Yang Liu"
] | 2309.11235 | 19,263 | https://openreview.net/forum?id=AOJyfhWYHf |
|
[] | Poster | [] | Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image. Previous methods have relied on domain-specific heuristics such as warping-based motion representation and 3D Morphable Models, which limit the naturalness and diversity of the generated avatars. In this work, we introduce GAIA (Generative AI for Avatar), which eliminates the domain priors in talking avatar generation. In light of the observation that the speech only drives the motion of the avatar while the appearance of the avatar and the background typically remain the same throughout the entire video, we divide our approach into two stages: 1) disentangling each frame into motion and appearance representations; 2) generating motion sequences conditioned on the speech and reference portrait image. We collect a large-scale high-quality talking avatar dataset and train the model on it with different scales (up to 2B parameters). Experimental results verify the superiority, scalability, and flexibility of GAIA as 1) the resulting model beats previous baseline models in terms of naturalness, diversity, lip-sync quality, and visual quality; 2) the framework is scalable since larger models yield better results; 3) it is general and enables different applications like controllable talking avatar generation and text-instructed avatar generation. | [] | [] | GAIA: Zero-shot Talking Avatar Generation | [
"Tianyu He",
"Junliang Guo",
"Runyi Yu",
"Yuchi Wang",
"jialiang zhu",
"Kaikai An",
"Leyi Li",
"Xu Tan",
"Chunyu Wang",
"Han Hu",
"HsiangTao Wu",
"sheng zhao",
"Jiang Bian"
] | 2311.15230 | 19,261 | https://openreview.net/forum?id=ATEawsFUj4 |
|
[] | Spotlight Poster | [
"https://github.com/VEWOXIC/FITS"
] | In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the principle that time series can be manipulated through interpolation in the complex frequency domain, achieving performance comparable to state-of-the-art models for time series forecasting and anomaly detection tasks. Notably, FITS accomplishes this with a svelte profile of just about $10k$ parameters, making it ideally suited for edge devices and paving the way for a wide range of applications. The code is available for review at: \url{https://anonymous.4open.science/r/FITS}. | [] | [] | FITS: Modeling Time Series with $10k$ Parameters | [
"Zhijian Xu",
"Ailing Zeng",
"Qiang Xu"
] | 2307.03756 | 18,329 | https://openreview.net/forum?id=bWcnvZ3qMb |
|
[] | Poster | [
"https://github.com/yue-zhongqi/diti"
] | Representation learning is all about discovering the hidden modular attributes that generate the data faithfully. We explore the potential of Denoising Diffusion Probabilistic Model (DM) in unsupervised learning of the modular attributes. We build a theoretical framework that connects the diffusion time-steps and the hidden attributes, which serves as an effective inductive bias for unsupervised learning. Specifically, the forward diffusion process incrementally adds Gaussian noise to samples at each time-step, which essentially collapses different samples into similar ones by losing attributes, e.g., fine-grained attributes such as texture are lost with less noise added (i.e., early time-steps), while coarse-grained ones such as shape are lost by adding more noise (i.e., late time-steps). To disentangle the modular attributes, at each time-step t, we learn a t-specific feature to compensate for the newly lost attribute, and the set of all {1,...,t}-specific features, corresponding to the cumulative set of lost attributes, are trained to make up for the reconstruction error of a pre-trained DM at time-step t. On CelebA, FFHQ, and Bedroom datasets, the learned feature significantly improves attribute classification and enables faithful counterfactual generation, e.g., interpolating only one specified attribute between two images, validating the disentanglement quality. Codes are in Appendix. | [] | [] | Exploring Diffusion Time-steps for Unsupervised Representation Learning | [
"Zhongqi Yue",
"Jiankun Wang",
"Qianru Sun",
"Lei Ji",
"Eric I-Chao Chang",
"Hanwang Zhang"
] | 2401.11430 | 18,328 | https://openreview.net/forum?id=bWzxhtl1HP |
|
[] | Poster | [] | Despite the remarkable capabilities of deep neural networks in image recognition, the dependence on activation functions remains a largely unexplored area and has yet to be eliminated. On the other hand, Polynomial Networks is a class of models that does not require activation functions, but have yet to perform on par with modern architectures. In this work, we aim close this gap and propose MONet, which relies *solely* on multilinear operators. The core layer of MONet, called Mu-Layer, captures multiplicative interactions of the elements of the input token. MONet captures high-degree interactions of the input elements and we demonstrate the efficacy of our approach on a series of image recognition and scientific computing benchmarks. The proposed model outperforms prior polynomial networks and performs on par with modern architectures. We believe that MONet can inspire further research on models that use entirely multilinear operations. | [] | [] | Multilinear Operator Networks | [
"Yixin Cheng",
"Grigorios Chrysos",
"Markos Georgopoulos",
"Volkan Cevher"
] | 2401.17992 | 18,326 | https://openreview.net/forum?id=bbCL5aRjUx |
|
[] | Spotlight Poster | [] | Residual neural networks are state-of-the-art deep learning models. Their continuous-depth analog, neural ordinary differential equations (ODEs), are also widely used. Despite their success, the link between the discrete and continuous models still lacks a solid mathematical foundation. In this article, we take a step in this direction by establishing an implicit regularization of deep residual networks towards neural ODEs, for nonlinear networks trained with gradient flow. We prove that if the network is initialized as a discretization of a neural ODE, then such a discretization holds throughout training. Our results are valid for a finite training time, and also as the training time tends to infinity provided that the network satisfies a Polyak-Łojasiewicz condition. Importantly, this condition holds for a family of residual networks where the residuals are two-layer perceptrons with an overparameterization in width that is only linear, and implies the convergence of gradient flow to a global minimum. Numerical experiments illustrate our results. | [] | [] | Implicit regularization of deep residual networks towards neural ODEs | [
"Pierre Marion",
"Yu-Han Wu",
"Michael Eli Sander",
"Gérard Biau"
] | 2309.01213 | 19,253 | https://openreview.net/forum?id=AbXGwqb5Ht |
|
[] | Poster | [] | Recent research in decoding methods for Natural Language Generation (NLG) tasks has shown that MAP decoding is not optimal, because model probabilities do not always align with human preferences. Stronger decoding methods, including Quality Estimation (QE) reranking and Minimum Bayes' Risk (MBR) decoding, have since been proposed to mitigate the model-perplexity-vs-quality mismatch. While these decoding methods achieve state-of-the-art performance, they are prohibitively expensive to compute. In this work, we propose MBR finetuning and QE finetuning, which distill the quality gains from these decoding methods at training time, while using an efficient decoding algorithm at inference time. Using the canonical NLG task of Neural Machine Translation (NMT), we show that even with self-training, these finetuning methods significantly outperform the base model. Moreover, when using an external LLM as a teacher model, these finetuning methods outperform finetuning on human-generated references. These findings suggest new ways to leverage monolingual data to achieve improvements in model quality that are on par with, or even exceed, improvements from human-curated data, while maintaining maximum efficiency during decoding. | [] | [] | MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods | [
"Mara Finkelstein",
"Markus Freitag"
] | 2309.10966 | 18,325 | https://openreview.net/forum?id=bkNx3O0sND |
|
[] | Poster | [
"https://github.com/Imageomics/INTR"
] | We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully-connected layer to incorporate class information to make predictions, we investigate a proactive approach, asking each class to search for itself in an image. We realize this idea via a Transformer encoder-decoder inspired by DEtection TRansformer (DETR). We learn "class-specific'' queries (one for each class) as input to the decoder, enabling each class to localize its patterns in an image via cross-attention. We name our approach INterpretable TRansformer (INTR), which is fairly easy to implement and exhibits several compelling properties. We show that INTR intrinsically encourages each class to attend distinctively; the cross-attention weights thus provide a faithful interpretation of the prediction. Interestingly, via "multi-head'' cross-attention, INTR could identify different "attributes'' of a class, making it particularly suitable for fine-grained classification and analysis, which we demonstrate on eight datasets. | [] | [] | A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis | [
"DIPANJYOTI PAUL",
"Arpita Chowdhury",
"Xinqi Xiong",
"Feng-Ju Chang",
"David Edward Carlyn",
"Samuel Stevens",
"Kaiya L Provost",
"Anuj Karpatne",
"Bryan Carstens",
"Daniel Rubenstein",
"Charles Stewart",
"Tanya Berger-Wolf",
"Yu Su",
"Wei-Lun Chao"
] | 2311.04157 | 18,324 | https://openreview.net/forum?id=bkdWThqE6q |
|
[] | Poster | [] | Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multi-task learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios.Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method. | [] | [] | Adaptive Stochastic Gradient Algorithm for Black-box Multi-Objective Learning | [
"Feiyang YE",
"Yueming Lyu",
"Xuehao Wang",
"Yu Zhang",
"Ivor Tsang"
] | 18,323 | https://openreview.net/forum?id=bm1JVsVZVu |
||
[] | Poster | [
"https://github.com/huanranchen/AdversarialAttacks"
] | It is widely recognized that deep learning models lack robustness to adversarial examples. An intriguing property of adversarial examples is that they can transfer across different models, which enables black-box attacks without any knowledge of the victim model. An effective strategy to improve the transferability is attacking an ensemble of models. However, previous works simply average the outputs of different models, lacking an in-depth analysis on how and why model ensemble methods can strongly improve the transferability. In this paper, we rethink the ensemble in adversarial attacks and define the common weakness of model ensemble with two properties: 1) the flatness of loss landscape; and 2) the closeness to the local optimum of each model. We empirically and theoretically show that both properties are strongly correlated with the transferability and propose a Common Weakness Attack (CWA) to generate more transferable adversarial examples by promoting these two properties. Experimental results on both image classification and object detection tasks validate the effectiveness of our approach to improving the adversarial transferability, especially when attacking adversarially trained models. We also successfully apply our method to attack a black-box large vision-language model -- Google's Bard, showing the practical effectiveness. | [] | [] | Rethinking Model Ensemble in Transfer-based Adversarial Attacks | [
"Huanran Chen",
"Yichi Zhang",
"Yinpeng Dong",
"Xiao Yang",
"Hang Su",
"Jun Zhu"
] | 2303.09105 | 19,251 | https://openreview.net/forum?id=AcJrSoArlh |
|
[] | Poster | [] | Graph Transformers (GTs) such as SAN and GPS have been shown to be universal function approximators. We show that when extending MPGNNs and even 2-layer MLPs with the same positional encodings that GTs use, they also become universal function approximators on graphs. All these results hold in the non-uniform case where a different network may be used for every graph size. In order to show meaningful differences between GTs and MPGNNs we then consider the uniform setting where a single network needs to work for all graph sizes. First, we show that none of the above models is universal in that setting. Then, our main technical result is that there are functions that GTs can express while MPGNNs with virtual nodes cannot and vice versa, making their uniform expressivity provably different. We show this difference empirically on synthetic data and observe that on real-world data global information exchange through graph transformers and conceptually simpler MPGNNs with virtual nodes achieve similar performance gains over message passing on various datasets. | [] | [] | Distinguished In Uniform: Self-Attention Vs. Virtual Nodes | [
"Eran Rosenbluth",
"Jan Tönshoff",
"Martin Ritzert",
"Berke Kisin",
"Martin Grohe"
] | 19,249 | https://openreview.net/forum?id=AcSChDWL6V |
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[] | Poster | [] | Of all the vector fields surrounding the minima of recurrent learning setups, the gradient field with its exploding and vanishing updates appears a poor choice for optimization, offering little beyond efficient computability. We seek to improve this suboptimal practice in the context of physics simulations, where backpropagating feedback through many unrolled time steps is considered crucial to acquiring temporally coherent behavior. The alternative vector field we propose follows from two principles: physics simulators, unlike neural networks, have a balanced gradient flow and certain modifications to the backpropagation pass leave the positions of the original minima unchanged. As any modification of backpropagation decouples forward and backward pass, the rotation-free character of the gradient field is lost. Therefore, we discuss the negative implications of using such a rotational vector field for optimization and how to counteract them. Our final procedure is easily implementable via a sequence of gradient stopping and component-wise comparison operations, which do not negatively affect scalability. Our experiments on three control problems show that especially as we increase the complexity of each task, the unbalanced updates from the gradient can no longer provide the precise control signals necessary while our method still solves the tasks. | [] | [] | Stabilizing Backpropagation Through Time to Learn Complex Physics | [
"Patrick Schnell",
"Nils Thuerey"
] | 18,322 | https://openreview.net/forum?id=bozbTTWcaw |
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[] | Poster | [] | Accelerated stochastic gradient descent (ASGD) is a workhorse in deep learning and often achieves better generalization performance than SGD. However, existing optimization theory can only explain the faster convergence of ASGD, but cannot explain its better generalization. In this paper, we study the generalization of ASGD for overparameterized linear regression, which is possibly the simplest setting of learning with overparameterization. We establish an instance-dependent excess risk bound for ASGD within each eigen-subspace of the data covariance matrix. Our analysis shows that (i) ASGD outperforms SGD in the subspace of small eigenvalues, exhibiting a faster rate of exponential decay for bias error, while in the subspace of large eigenvalues, its bias error decays slower than SGD; and (ii) the variance error of ASGD is always larger than that of SGD. Our result suggests that ASGD can outperform SGD when the difference between the initialization and the true weight vector is mostly confined to the subspace of small eigenvalues. Additionally, when our analysis is specialized to linear regression in the strongly convex setting, it yields a tighter bound for bias error than the best-known result. | [] | [] | Risk Bounds of Accelerated SGD for Overparameterized Linear Regression | [
"Xuheng Li",
"Yihe Deng",
"Jingfeng Wu",
"Dongruo Zhou",
"Quanquan Gu"
] | 2311.14222 | 19,248 | https://openreview.net/forum?id=AcoXPIPh4A |
|
[] | Poster | [] | A recent paper by Farina and Pipis (2023) established the existence of uncoupled no-linear-swap regret dynamics with polynomial-time iterations in extensive-form games. The equilibrium points reached by these dynamics, known as linear correlated equilibria, are currently the tightest known relaxation of correlated equilibrium that can be learned in polynomial time in any finite extensive-form game. However, their properties remain vastly unexplored, and their computation is onerous. In this paper, we provide several contributions shedding light on the fundamental nature of linear-swap regret. First, we show a connection between linear deviations and a generalization of communication deviations in which the player can make queries to a ``mediator'' who replies with action recommendations, and, critically, the player is not constrained to match the timing of the game as would be the case for communication deviations. We coin this latter set the untimed communication (UTC) deviations. We show that the UTC deviations coincide precisely with the linear deviations, and therefore that any player minimizing UTC regret also minimizes linear-swap regret. We then leverage this connection to develop state-of-the-art no-regret algorithms for computing linear correlated equilibria, both in theory and in practice. In theory, our algorithms achieve polynomially better per-iteration runtimes; in practice, our algorithms represent the state of the art by several orders of magnitude. | [] | [] | Mediator Interpretation and Faster Learning Algorithms for Linear Correlated Equilibria in General Sequential Games | [
"Brian Hu Zhang",
"Gabriele Farina",
"Tuomas Sandholm"
] | 18,321 | https://openreview.net/forum?id=bsKMPAFHO7 |
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[] | Spotlight Poster | [
"https://github.com/ziqipang/LM4VisualEncoding"
] | This paper reveals that large language models (LLMs), despite being trained solely on text data, are \emph{surprisingly} strong encoders for \emph{purely} visual tasks in the absence of language. Even more intriguingly, this can be achieved by a simple yet previously overlooked strategy -- employing a \emph{frozen} transformer block from \emph{pre-trained} LLMs as a constituent encoder layer to directly process visual tokens. Our work pushes the boundaries of leveraging LLMs for computer vision tasks, significantly departing from conventional practices that typically necessitate a multi-modal vision-language setup with associated language prompts, inputs, or outputs. We demonstrate that our approach consistently enhances performance across \emph{a diverse range of tasks}, encompassing pure 2D or 3D visual recognition tasks (e.g., image and point cloud classification), temporal modeling tasks (e.g., action recognition), non-semantic tasks (e.g., motion forecasting), and multi-modal tasks (e.g., 2D/3D visual question answering and image-text retrieval). Such improvements are a general phenomenon, applicable to various types of LLMs (e.g., LLaMA and OPT) and different LLM transformer blocks. We additionally propose the \emph{information filtering} hypothesis to explain the effectiveness of pre-trained LLMs in visual encoding -- the pre-trained LLM transformer blocks discern informative visual tokens and further amplify their effect. This hypothesis is empirically supported by the observation that the feature activation, after training with LLM transformer blocks, exhibits a stronger focus on relevant regions. We hope that our work inspires new perspectives on utilizing LLMs and deepening our understanding of their underlying mechanisms. | [] | [] | Frozen Transformers in Language Models Are Effective Visual Encoder Layers | [
"Ziqi Pang",
"Ziyang Xie",
"Yunze Man",
"Yu-Xiong Wang"
] | 2310.12973 | 17,627 | https://openreview.net/forum?id=t0FI3Q66K5 |
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[] | Poster | [] | Natural Language Explanation (NLE) in vision and language tasks aims to provide human-understandable explanations for the associated decision-making process. In practice, one might encounter explanations which lack informativeness or contradict visual-grounded facts, known as \textit{implausibility} and \textit{hallucination} problems, respectively. To tackle these challenging issues, we consider the task of visual question answering (VQA) and introduce \textit{Rapper}, a two-stage \textbf{R}einforced R\textbf{a}tionale-\textbf{P}rom\textbf{p}t\textbf{e}d Pa\textbf{r}adigm. By knowledge distillation, the former stage of \textit{Rapper} infuses rationale-prompting via large language models (LLMs), encouraging the rationales supported by language-based facts. As for the latter stage, a unique Reinforcement Learning from NLE Feedback (RLNF) is introduced for injecting visual facts into NLE generation. Finally, quantitative and qualitative experiments on two VL-NLE benchmarks show that \textsc{Rapper} surpasses state-of-the-art VQA-NLE methods while providing plausible and faithful NLE. | [] | [] | RAPPER: Reinforced Rationale-Prompted Paradigm for Natural Language Explanation in Visual Question Answering | [
"Kai-Po Chang",
"Chi-Pin Huang",
"Wei-Yuan Cheng",
"Fu-En Yang",
"Chien-Yi Wang",
"Yung-Hsuan Lai",
"Yu-Chiang Frank Wang"
] | 18,320 | https://openreview.net/forum?id=bshfchPM9H |
||
[] | Poster | [] | Temporal abstraction and efficient planning pose significant challenges in offline reinforcement learning, mainly when dealing with domains that involve temporally extended tasks and delayed sparse rewards. Existing methods typically plan in the raw action space and can be inefficient and inflexible. Latent action spaces offer a more flexible approach, capturing only possible actions within the behavior policy support and decoupling the temporal structure between planning and modeling. However, current latent-action-based methods are limited to discrete spaces and require expensive planning steps. This paper presents a unified framework for continuous latent action space representation learning and planning by leveraging latent, score-based diffusion models. We establish the theoretical equivalence between planning in the latent action space and energy-guided sampling with a pretrained diffusion model and introduce a novel sequence-level exact sampling method. Our proposed method, $\texttt{LatentDiffuser}$, demonstrates competitive performance on low-dimensional locomotion control tasks and surpasses existing methods in higher-dimensional tasks. | [] | [] | Efficient Planning with Latent Diffusion | [
"Wenhao Li"
] | 2310.00311 | 18,319 | https://openreview.net/forum?id=btpgDo4u4j |
|
[] | Poster | [
"https://github.com/zqhang/AnomalyCLIP"
] | Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliarydata to detect anomalies without any training sample in a target dataset. Itis a crucial task when training data is not accessible due to various concerns, e.g.,data privacy, yet it is challenging since the models need to generalize to anomaliesacross different domains where the appearance of foreground objects, abnormalregions, and background features, such as defects/tumors on different products/organs, can vary significantly. Recently large pre-trained vision-languagemodels (VLMs), such as CLIP, have demonstrated strong zero-shot recognitionability in various vision tasks, including anomaly detection. However, their ZSADperformance is weak since the VLMs focus more on modeling the class semanticsof the foreground objects rather than the abnormality/normality in the images. Inthis paper we introduce a novel approach, namely AnomalyCLIP, to adapt CLIPfor accurate ZSAD across different domains. The key insight of AnomalyCLIPis to learn object-agnostic text prompts that capture generic normality and abnormalityin an image regardless of its foreground objects. This allows our model tofocus on the abnormal image regions rather than the object semantics, enablinggeneralized normality and abnormality recognition on diverse types of objects.Large-scale experiments on 17 real-world anomaly detection datasets show thatAnomalyCLIP achieves superior zero-shot performance of detecting and segmentinganomalies in datasets of highly diverse class semantics from various defectinspection and medical imaging domains. | [] | [] | AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection | [
"Qihang Zhou",
"Guansong Pang",
"Yu Tian",
"Shibo He",
"Jiming Chen"
] | 2310.18961 | 18,318 | https://openreview.net/forum?id=buC4E91xZE |
|
[] | Poster | [] | Graph Neural Networks (GNNs) have seen significant achievements in semi-supervised node classification. Yet, their efficacy often hinges on access to high-quality labeled node samples, which may not always be available in real-world scenarios. While active learning is commonly employed across various domains to pinpoint and label high-quality samples based on data features, graph data present unique challenges due to their intrinsic structures that render nodes non-i.i.d. Furthermore, biases emerge from the positioning of labeled nodes; for instance, nodes closer to the labeled counterparts often yield better performance. To better leverage graph structure and mitigate structural bias in active learning, we present a unified optimization framework (SCARCE), which is also easily incorporated with node features. Extensive experiments demonstrate that the proposed method not only improves the GNNs performance but also paves the way for more fair results. | [] | [] | Structural Fairness-aware Active Learning for Graph Neural Networks | [
"Haoyu Han",
"Xiaorui Liu",
"Li Ma",
"MohamadAli Torkamani",
"Hui Liu",
"Jiliang Tang",
"Makoto Yamada"
] | 18,317 | https://openreview.net/forum?id=bvjcMvMn7B |
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[] | Spotlight Poster | [] | Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' training dataset such that the trained models have inference failures. While previous studies have executed different types of attacks, one major challenge that greatly limits their effectiveness is the uncertainty of the re-training process after the injection of poisoning samples. It includes the uncertainty of training initialization, algorithm and model architecture. To address this challenge, we propose a new strategy called **Sharpness-Aware Data Poisoning Attack (SAPA)**. In particular, it leverages the concept of DNNs' loss landscape sharpness to optimize the poisoning effect on the (approximately) worst re-trained model. Extensive experiments demonstrate that SAPA offers a general and principled strategy that significantly enhances various types of poisoning attacks against various types of re-training uncertainty. | [] | [] | Sharpness-Aware Data Poisoning Attack | [
"Pengfei He",
"Han Xu",
"Jie Ren",
"Yingqian Cui",
"Shenglai Zeng",
"Hui Liu",
"Charu C. Aggarwal",
"Jiliang Tang"
] | 2305.14851 | 18,316 | https://openreview.net/forum?id=bxITGFPVWh |
|
[] | Poster | [] | Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences. To address these issues, we propose AlignDiff, a novel framework that leverages RLHF to quantify human preferences, covering abstractness, and utilizes them to guide diffusion planning for zero-shot behavior customizing, covering mutability. AlignDiff can accurately match user-customized behaviors and efficiently switch from one to another. To build the framework, we first establish the multi-perspective human feedback datasets, which contain comparisons for the attributes of diverse behaviors, and then train an attribute strength model to predict quantified relative strengths. After relabeling behavioral datasets with relative strengths, we proceed to train an attribute-conditioned diffusion model, which serves as a planner with the attribute strength model as a director for preference aligning at the inference phase. We evaluate AlignDiff on various locomotion tasks and demonstrate its superior performance on preference matching, switching, and covering compared to other baselines. Its capability of completing unseen downstream tasks under human instructions also showcases the promising potential for human-AI collaboration. More visualization videos are released on https://aligndiff.github.io/. | [] | [] | AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model | [
"Zibin Dong",
"Yifu Yuan",
"Jianye HAO",
"Fei Ni",
"Yao Mu",
"YAN ZHENG",
"Yujing Hu",
"Tangjie Lv",
"Changjie Fan",
"Zhipeng Hu"
] | 2310.02054 | 18,315 | https://openreview.net/forum?id=bxfKIYfHyx |
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[] | Poster | [] | Reinforcement learning (RL) for complex tasks remains a challenge, primarily due to the difficulties of engineering scalar reward functions and the inherent inefficiency of training models from scratch. Instead, it would be better to specify complex tasks in terms of elementary subtasks and to reuse subtask solutions whenever possible. In this work, we address continuous space lexicographic multi-objective RL problems, consisting of prioritized subtasks, which are notoriously difficult to solve. We show that these can be scalarized with a subtask transformation and then solved incrementally using value decomposition. Exploiting this insight, we propose prioritized soft Q-decomposition (PSQD), a novel algorithm for learning and adapting subtask solutions under lexicographic priorities in continuous state-action spaces. PSQD offers the ability to reuse previously learned subtask solutions in a zero-shot composition, followed by an adaptation step. Its ability to use retained subtask training data for offline learning eliminates the need for new environment interaction during adaptation. We demonstrate the efficacy of our approach by presenting successful learning, reuse, and adaptation results for both low- and high-dimensional simulated robot control tasks, as well as offline learning results. In contrast to baseline approaches, PSQD does not trade off between conflicting subtasks or priority constraints and satisfies subtask priorities during learning. PSQD provides an intuitive framework for tackling complex RL problems, offering insights into the inner workings of the subtask composition. | [] | [] | Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning | [
"Finn Rietz",
"Erik Schaffernicht",
"Stefan Heinrich",
"Johannes A. Stork"
] | 18,314 | https://openreview.net/forum?id=c0MyyXyGfn |
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[] | Poster | [] | If generalist robots are to operate in truly unstructured environments, they need to be able to recognize and reason about novel objects and scenarios. Such objects and scenarios might not be present in the robot's own training data. We propose SuSIE, a method that leverages an image editing diffusion model to act as a high-level planner by proposing intermediate subgoals that a low-level controller attains. Specifically, we fine-tune InstructPix2Pix on robot data such that it outputs a hypothetical future observation given the robot's current observation and a language command. We then use the same robot data to train a low-level goal-conditioned policy to reach a given image observation. We find that when these components are combined, the resulting system exhibits robust generalization capabilities. The high-level planner utilizes its Internet-scale pre-training and visual understanding to guide the low-level goal-conditioned policy, achieving significantly better generalization than conventional language-conditioned policies. We demonstrate that this approach solves real robot control tasks involving novel objects, distractors, and even environments, both in the real world and in simulation. The project website can be found at http://subgoal-image-editing.github.io. | [] | [] | Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models | [
"Kevin Black",
"Mitsuhiko Nakamoto",
"Pranav Atreya",
"Homer Rich Walke",
"Chelsea Finn",
"Aviral Kumar",
"Sergey Levine"
] | 18,313 | https://openreview.net/forum?id=c0chJTSbci |
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[] | Poster | [
"https://github.com/Raman1121/FairTune"
] | Training models with robust group fairness properties is crucial in ethically sensitive application areas such as medical diagnosis. Despite the growing body of work aiming to minimise demographic bias in AI, this problem remains challenging. A key reason for this challenge is the fairness generalisation gap: High-capacity deep learning models can fit all training data nearly perfectly, and thus also exhibit perfect fairness during training. In this case, bias emerges only during testing when generalisation performance differs across sub-groups. This motivates us to take a bi-level optimisation perspective on fair learning: Optimising the learning strategy based on validation fairness. Specifically, we consider the highly effective workflow of adapting pre-trained models to downstream medical imaging tasks using parameter-efficient fine-tuning (PEFT) techniques. There is a trade-off between updating more parameters, enabling a better fit to the task of interest vs. fewer parameters, potentially reducing the generalisation gap. To manage this tradeoff, we propose FairTune, a framework to optimise the choice of PEFT parameters with respect to fairness. We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets. | [] | [] | FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis | [
"Raman Dutt",
"Ondrej Bohdal",
"Sotirios A. Tsaftaris",
"Timothy Hospedales"
] | 2310.05055 | 19,237 | https://openreview.net/forum?id=ArpwmicoYW |
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[] | Poster | [] | We report the presence of a simple neural mechanism that represents an input-output function as a vector within autoregressive transformer language models (LMs). Using causal mediation analysis on a diverse range of in-context-learning (ICL) tasks, we find that a small number attention heads transport a compact representation of the demonstrated task, which we call a function vector (FV). FVs are robust to changes in context, i.e., they trigger execution of the task on inputs such as zero-shot and natural text settings that do not resemble the ICL contexts from which they are collected. We test FVs across a range of tasks, models, and layers and find strong causal effects across settings in middle layers. We investigate the internal structure of FVs and find while that they often contain information that encodes the output space of the function, this information alone is not sufficient to reconstruct an FV. Finally, we test semantic vector composition in FVs, and find that to some extent they can be summed to create vectors that trigger new complex tasks. Our findings show that compact, causal internal vector representations of function abstractions can be explicitly extracted from LLMs. | [] | [] | Function Vectors in Large Language Models | [
"Eric Todd",
"Millicent Li",
"Arnab Sen Sharma",
"Aaron Mueller",
"Byron C Wallace",
"David Bau"
] | 2310.15213 | 19,235 | https://openreview.net/forum?id=AwyxtyMwaG |
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[] | Poster | [] | Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for data generation across various domains. However, a significant bottleneck is the necessity for whole-network computation during every step of the generative process, leading to high computational overheads. This paper presents a novel framework, Denoising Diffusion Step-aware Models (DDSM), to address this challenge. Unlike conventional approaches, DDSM employs a spectrum of neural networks whose sizes are adapted according to the importance of each generative step, as determined through evolutionary search. This step-wise network variation effectively circumvents redundant computational efforts, particularly in less critical steps, thereby enhancing the efficiency of the diffusion model. Furthermore, the step-aware design can be seamlessly integrated with other efficiency-geared diffusion models such as DDIMs and latent diffusion, thus broadening the scope of computational savings. Empirical evaluations demonstrate that DDSM achieves computational savings of 49% for CIFAR-10, 61% for CelebA-HQ, 59% for LSUN-bedroom, 71% for AFHQ, and 76% for ImageNet, all without compromising the generation quality. Our code and models will be publicly available. | [] | [] | Denoising Diffusion Step-aware Models | [
"Shuai Yang",
"Yukang Chen",
"Luozhou WANG",
"Shu Liu",
"Ying-Cong Chen"
] | 2310.03337 | 18,311 | https://openreview.net/forum?id=c43FGk8Pcg |
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[] | Poster | [] | Analyzing multivariate time series is important in many domains. However, it has been difficult to learn robust and generalizable representations within multivariate datasets due to complex inter-channel relationships and dynamic shifts. In this paper, we introduce a novel approach for learning spatiotemporal structure and using it to improve the application of transformers to timeseries datasets. Our framework learns a set of group tokens, and builds an instance-specific group embedding (GE) layer that assigns input tokens to a small number of group tokens to incorporate structure into learning. We then introduce a novel architecture, Group-Aware transFormer (GAFormer), which incorporates both spatial and temporal group embeddings to achieve state-of-the-art performance on a number of time-series classification and regression tasks. In evaluations on a number of diverse timeseries datasets, we show that GE on its own can provide a nice enhancement to a number of backbones, and that by coupling spatial and temporal group embeddings, the GAFormer can outperform the existing baselines. Finally, we show how our approach discerns latent structures in data even without information about the spatial ordering of channels, and yields a more interpretable decomposition of spatial and temporal structure underlying complex multivariate datasets. | [] | [] | GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings | [
"Jingyun Xiao",
"Ran Liu",
"Eva L Dyer"
] | 18,310 | https://openreview.net/forum?id=c56TWtYp0W |
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[] | Poster | [] | Proteins can be represented in various ways, including their sequences, 3D structures, and surfaces. While recent studies have successfully employed sequence- or structure-based representations to address multiple tasks in protein science, there has been significant oversight in incorporating protein surface information, a critical factor for protein function. In this paper, we present a pre-training strategy that incorporates information from protein sequences, 3D structures, and surfaces to improve protein representation learning. Specifically, we utilize Implicit Neural Representations (INRs) for learning surface characteristics, and name it ProteinINR. We confirm that ProteinINR successfully reconstructs protein surfaces, and integrate this surface learning into the existing pre-training strategy of sequences and structures. Our results demonstrate that our approach can enhance performance in various downstream tasks, thereby underscoring the importance of including surface attributes in protein representation learning. These findings underline the importance of understanding protein surfaces for generating effective protein representations. | [] | [] | Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning | [
"Youhan Lee",
"Hasun Yu",
"Jaemyung Lee",
"Jaehoon Kim"
] | 19,226 | https://openreview.net/forum?id=BEH4mGo7zP |
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[] | Poster | [
"https://github.com/TianyuFan0504/WAS"
] | Recent years have witnessed the great success of graph pre-training for graph representation learning. With hundreds of graph pre-training tasks proposed, integrating knowledge acquired from multiple pre-training tasks has become a popular research topic. We identify two important collaborative processes for this topic: (1) select: how to select an optimal task combination from a given task pool based on their compatibility, and (2) weigh: how to weigh the importance of the selected tasks based on their importance. While there has been a lot of current works focused on weighing, comparatively little effort has been devoted to selecting. In this paper, we propose a novel instance-level framework for integrating multiple graph pre-training tasks, Weigh And Select (WAS), where the two collaborative processes, weighing and selecting, are combined by decoupled siamese networks. Specifically, it first adaptively learns an optimal combination of tasks for each instance from a given task pool, based on which a customized instance-level task weighing strategy is learned. Extensive experiments on 16 graph datasets across node-level and graph-level show that by combining a few simple but classical tasks, WAS can achieve comparable performance to other leading counterparts. | [] | [] | Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks | [
"Tianyu Fan",
"Lirong Wu",
"Yufei Huang",
"Haitao Lin",
"Cheng Tan",
"Zhangyang Gao",
"Stan Z. Li"
] | 2403.01400 | 18,307 | https://openreview.net/forum?id=c85tdYOOju |
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[] | Poster | [] | Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the Code Usage Frequency of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit $\underline{\text{c}}$ode-based $\underline{\text{s}}$elf-$\underline{\text{v}}$erification (CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as "False", the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset $\textbf{(53.9}$% → $\textbf{84.3}$%$\textbf{)}$. | [] | [] | Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification | [
"Aojun Zhou",
"Ke Wang",
"Zimu Lu",
"Weikang Shi",
"Sichun Luo",
"Zipeng Qin",
"Shaoqing Lu",
"Anya Jia",
"Linqi Song",
"Mingjie Zhan",
"Hongsheng Li"
] | 18,306 | https://openreview.net/forum?id=c8McWs4Av0 |
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[] | Poster | [] | As neural networks grow in scale, their training becomes both computationally demanding and rich in dynamics. Amidst the flourishing interest in these training dynamics, we present a novel observation: Parameters during training exhibit intrinsic correlations over time. Capitalizing on this, we introduce \emph{correlation mode decomposition} (CMD). This algorithm clusters the parameter space into groups, termed modes, that display synchronized behavior across epochs. This enables CMD to efficiently represent the training dynamics of complex networks, like ResNets and Transformers, using only a few modes. Moreover, test set generalization is enhanced. We introduce an efficient CMD variant, designed to run concurrently with training. Our experiments indicate that CMD surpasses the state-of-the-art method for compactly modeled dynamics on image classification. Our modeling can improve training efficiency and lower communication overhead, as shown by our preliminary experiments in the context of federated learning. | [] | [] | Enhancing Neural Training via a Correlated Dynamics Model | [
"Jonathan Brokman",
"Roy Betser",
"Rotem Turjeman",
"Tom Berkov",
"Ido Cohen",
"Guy Gilboa"
] | 2312.13247 | 18,304 | https://openreview.net/forum?id=c9xsaASm9L |
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[] | Poster | [] | We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost the primary task performance which we focus on, while preserving a single task inference cost of the primary task. While most existing auxiliary learning methods are optimization-based relying on loss weights/gradients manipulation, our method is architecture-based with a flexible asymmetric structure for the primary and auxiliary tasks, which produces different networks for training and inference. Specifically, starting from two single task networks/branches (each representing a task), we propose a novel method with evolving networks where only primary-to-auxiliary links exist as the cross-task connections after convergence. These connections can be removed during the primary task inference, resulting in a single task inference cost. We achieve this by formulating a Neural Architecture Search (NAS) problem, where we initialize bi-directional connections in the search space and guide the NAS optimization converging to an architecture with only the single-side primary-to-auxiliary connections. Moreover, our method can be incorporated with existing optimization-based auxiliary learning approaches. Extensive experiments with 6 tasks on NYU v2, CityScapes, and Taskonomy datasets using VGG-16, ResNet-50, and ViTBase backbones validate the promising performance. The codes will be released. | [] | [] | Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost | [
"Yuan Gao",
"WEIZHONG ZHANG",
"Wenhan Luo",
"Lin Ma",
"Jin-Gang Yu",
"Gui-Song Xia",
"Jiayi Ma"
] | 18,302 | https://openreview.net/forum?id=cINwAhrgLf |
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[] | Spotlight Poster | [] | A common assumption of many anomaly detection methods is that a reasonable decision boundary has a hypersphere shape, which is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces. In this paper, we first propose a novel deep anomaly detection model that improves the original hypersphere learning through an orthogonal projection layer, which ensures that the training data distribution is consistent with the hypersphere hypothesis, thereby increasing the true positive rate and decreasing the false negative rate. Moreover, we propose a bi-hypersphere compression method to obtain a hyperspherical shell that yields a more compact decision region than a hyperball, which is demonstrated theoretically and numerically. Note that the proposed methods are not confined to common datasets, such as image and tabular data, but are also extended to a more challenging but promising scenario, graph-level anomaly detection, which learns graph representation with maximum mutual information between the substructure and global structure features while exploring orthogonal single- or bi-hypersphere anomaly decision boundaries. The numerical and visualization results on benchmark datasets demonstrate the effectiveness and superiority of our methods in comparison with many baselines and the state-of-the-arts. | [] | [] | Deep Orthogonal Hypersphere Compression for Anomaly Detection | [
"Yunhe Zhang",
"Yan Sun",
"Jinyu Cai",
"Jicong Fan"
] | 2302.06430 | 18,301 | https://openreview.net/forum?id=cJs4oE4m9Q |
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[] | Poster | [] | Visual prostheses are potential devices to restore vision for the blind, which highly depends on the quality of stimulation patterns. However, existing processing frameworks utilize region detection or deep learning model to generate stimulation patterns without effective optimization methods to achieve better vision recovery. In this paper, we propose for the first time an end-to-end stimulation patterns optimization framework that consists of a retinal network to mimic the behavior of retina, phosphene model to simulate phosphene generated by retinal prostheses, and primary vision system network (PVS-net) to mimic the function from retina to visual cortex. Combining these three components, the framework can simulate the whole process of visual signals processing from external scenes to the visual perception in the cortex. Besides, we adopt biological spike responses of the visual cortex as target signals during training, providing an efficient way to generate and verify the quality of stimulation patterns. The proposed retina network adopts a spike representation encoding technique to record external scenes and a spiking recurrent neural network to predict the stimulation patterns. The phosphene model and VVS-net simulate the phosphene in the retina and predict multiple V1 neurons' response. Experimental results show that the generated stimulation patterns not only contain the feature of original scenes but also have biological plausibility to generate similar perceptions in the visual cortex. The performance of the proposed framework achieves 0.78, evaluated by the Pearson correlation coefficient between predicted values and the recorded response of normal neurons. | [] | [] | Exploring Effective Stimulus Encoding via Vision System Modeling for Visual Prostheses | [
"Chuanqing Wang",
"Di Wu",
"Chaoming Fang",
"Jie Yang",
"Mohamad Sawan"
] | 18,300 | https://openreview.net/forum?id=cKAUvMePUN |
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[] | Poster | [] | The rising rates of diabetes necessitate innovative methods for its management. Continuous glucose monitors (CGM) are small medical devices that measure blood glucose levels at regular intervals providing insights into daily patterns of glucose variation. Forecasting of glucose trajectories based on CGM data holds the potential to substantially improve diabetes management, by both refining artificial pancreas systems and enabling individuals to make adjustments based on predictions to maintain optimal glycemic range. Despite numerous methods proposed for CGM-based glucose trajectory prediction, these methods are typically evaluated on small, private datasets, impeding reproducibility, further research, and practical adoption. The absence of standardized prediction tasks and systematic comparisons between methods has led to uncoordinated research efforts, obstructing the identification of optimal tools for tackling specific challenges. As a result, only a limited number of prediction methods have been implemented in clinical practice. | [] | [] | GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks | [
"Renat Sergazinov",
"Elizabeth Chun",
"Valeriya Rogovchenko",
"Nathaniel J Fernandes",
"Nicholas Kasman",
"Irina Gaynanova"
] | 18,296 | https://openreview.net/forum?id=cUSNs8nGaV |
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[] | Poster | [] | In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local models has focused on obtaining the lower bounds for the case where the underlying parameter is $1$-sparse, and extending such bounds to the more general $k$-sparse case has proven to be challenging. Moreover, it is unclear whether efficient non-interactive LDP (NLDP) algorithms exist. To address these issues, we first consider the problem in the $\epsilon$ non-interactive LDP model and provide a lower bound of $\Omega(\frac{\sqrt{dk\log d}}{\sqrt{n}\epsilon})$ on the $\ell_2$-norm estimation error for sub-Gaussian data, where $n$ is the sample size and $d$ is the dimension of the space. We propose an innovative NLDP algorithm, the very first of its kind for the problem. As a remarkable outcome, this algorithm also yields a novel and highly efficient estimator as a valuable by-product. Our algorithm achieves an upper bound of $\tilde{O}({\frac{d\sqrt{k}}{\sqrt{n}\epsilon}})$ for the estimation error when the data is sub-Gaussian, which can be further improved by a factor of $O(\sqrt{d})$ if the server has additional public but unlabeled data. For the sequentially interactive LDP model, we show a similar lower bound of $\Omega({\frac{\sqrt{dk}}{\sqrt{n}\epsilon}})$. As for the upper bound, we rectify a previous method and show that it is possible to achieve a bound of $\tilde{O}(\frac{k\sqrt{d}}{\sqrt{n}\epsilon})$. Our findings reveal fundamental differences between the non-private case, central DP model, and local DP model in the sparse linear regression problem. | [] | [] | Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model | [
"Liyang Zhu",
"Meng Ding",
"Vaneet Aggarwal",
"Jinhui Xu",
"Di Wang"
] | 2310.07367 | 18,295 | https://openreview.net/forum?id=cVUOnF7iVp |
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[] | Poster | [] | The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient representations. While numerous traditional manifold-based techniques exist for dimensionality reduction, their application in self-supervised learning has witnessed slow progress. The recent MSimCLR method combines manifold encoding with SimCLR but requires extremely low target encoding dimensions to outperform SimCLR, limiting its applicability. This paper introduces a novel learning paradigm using an unbalanced atlas (UA), capable of surpassing state-of-the-art self-supervised learning approaches. We investigated and engineered the DeepInfomax with an unbalanced atlas (DIM-UA) method by adapting the Spatiotemporal DeepInfomax (ST-DIM) framework to align with our proposed UA paradigm. The efficacy of DIM-UA is demonstrated through training and evaluation on the Atari Annotated RAM Interface (AtariARI) benchmark, a modified version of the Atari 2600 framework that produces annotated image samples for representation learning. The UA paradigm improves existing algorithms significantly as the number of target encoding dimensions grows. For instance, the mean F1 score averaged over categories of DIM-UA is~75% compared to ~70% of ST-DIM when using 16384 hidden units. | [] | [] | State Representation Learning Using an Unbalanced Atlas | [
"Li Meng",
"Morten Goodwin",
"Anis Yazidi",
"Paal E. Engelstad"
] | 2305.10267 | 18,294 | https://openreview.net/forum?id=cWdAYDLmPa |
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[] | Poster | [] | Vision-language models such as CLIP have shown great impact on diverse downstream tasks for zero-shot or label-free predictions. However, when it comes to low-level vision such as image restoration their performance deteriorates dramatically due to corrupted inputs. In this paper, we present a degradation-aware vision-language model (DA-CLIP) to better transfer pretrained vision-language models to low-level vision tasks as a universal framework for image restoration. More specifically, DA-CLIP trains an additional controller that adapts the fixed CLIP image encoder to predict high-quality feature embeddings. By integrating the embedding into an image restoration network via cross-attention, we are able to pilot the model to learn a high-fidelity image reconstruction. The controller itself will also output a degradation feature that matches the real corruptions of the input, yielding a natural classifier for different degradation types. In addition, we construct a mixed degradation dataset with synthetic captions for DA-CLIP training. Our approach advances state-of-the-art performance on both degradation-specific and unified image restoration tasks, showing a promising direction of prompting image restoration with large-scale pretrained vision-language models. | [] | [] | Controlling Vision-Language Models for Multi-Task Image Restoration | [
"Ziwei Luo",
"Fredrik K. Gustafsson",
"Zheng Zhao",
"Jens Sjölund",
"Thomas B. Schön"
] | 2310.01018 | 17,626 | https://openreview.net/forum?id=t3vnnLeajU |
|
[] | Poster | [
"https://github.com/divelab/AIRS"
] | Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants; namely, iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations. Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. | [] | [] | Complete and Efficient Graph Transformers for Crystal Material Property Prediction | [
"Keqiang Yan",
"Cong Fu",
"Xiaofeng Qian",
"Xiaoning Qian",
"Shuiwang Ji"
] | 2403.11857 | 19,203 | https://openreview.net/forum?id=BnQY9XiRAS |
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[] | Poster | [] | The advent of instruction-tuned language models that convincingly mimic human writing poses a significant risk of abuse. For example, such models could be used for plagiarism, disinformation, spam, or phishing. However, such abuse may be counteracted with the ability to detect whether a piece of text was composed by a language model rather than a human. Some previous approaches to this problem have relied on supervised methods trained on corpora of confirmed human and machine-written documents. Unfortunately, model under-specification poses an unavoidable challenge for such detectors, making them brittle in the face of data shifts, such as the release of further language models producing still more fluent text than the models used to train the detectors. Other previous approaches require access to the models that generated the text to be detected at inference or detection time, which is often impractical. In light of these challenge, we pursue a fundamentally different approach not relying on samples from language models of concern at training time. Instead, we propose to leverage representations of writing style estimated from human-authored text. Indeed, we find that features effective at distinguishing among human authors are also effective at distinguishing human from machine authors, including state of the art large language models like Llama 2, ChatGPT, and GPT-4. Furthermore, given handfuls of examples composed by each of several specific language models of interest, our approach affords the ability to predict which model specifically generated a given document. | [] | [] | Few-Shot Detection of Machine-Generated Text using Style Representations | [
"Rafael Alberto Rivera Soto",
"Kailin Koch",
"Aleem Khan",
"Barry Y. Chen",
"Marcus Bishop",
"Nicholas Andrews"
] | 2401.06712 | 18,293 | https://openreview.net/forum?id=cWiEN1plhJ |
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[] | Poster | [] | We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal approximation in terms of total variation distance for the ReLU non-linearity. This allows propagating Gaussian and Cauchy input uncertainties through neural networks to quantify their output uncertainties. To demonstrate the utility of propagating distributions, we apply the proposed method to predicting calibrated confidence intervals and selective prediction on out-of-distribution data. The results demonstrate a broad applicability of propagating distributions and show the advantages of our method over other approaches such as moment matching. | [] | [] | Uncertainty Quantification via Stable Distribution Propagation | [
"Felix Petersen",
"Aashwin Ananda Mishra",
"Hilde Kuehne",
"Christian Borgelt",
"Oliver Deussen",
"Mikhail Yurochkin"
] | 2402.08324 | 18,291 | https://openreview.net/forum?id=cZttUMTiPL |
|
[] | Poster | [
"https://github.com/marcusm117/IdentityChain"
] | Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the conventional accuracy evaluates the performance of Code LLMs on a set of individual tasks, their self-consistency across different tasks is overlooked. Intuitively, a trustworthy model should be self-consistent when generating natural language specifications for its own code and generating code for its own specifications. Failure to preserve self-consistency reveals a lack of understanding of the shared semantics underlying natural language and programming language, and therefore undermines the trustworthiness of a model. In this paper, we first formally define the self-consistency of Code LLMs and then design a framework, IdentityChain, which effectively and efficiently evaluates the self-consistency and conventional accuracy of a model at the same time. We study eleven Code LLMs and show that they fail to preserve self-consistency, which is indeed a distinct aspect from conventional accuracy. Furthermore, we show that IdentityChain can be used as a model debugging tool to expose weaknesses of Code LLMs by demonstrating three major weaknesses that we identify in current models using IdentityChain. Our code is available at https://github.com/marcusm117/IdentityChain. | [] | [] | Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain | [
"Marcus J. Min",
"Yangruibo Ding",
"Luca Buratti",
"Saurabh Pujar",
"Gail Kaiser",
"Suman Jana",
"Baishakhi Ray"
] | 2310.14053 | 18,290 | https://openreview.net/forum?id=caW7LdAALh |
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[] | Poster | [] | Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with these agents. For example, agents may dominate the collaboration and exhibit unintended or detrimental behaviors, leading to poor experiences for their human partners. In other words, most game AI agents are modeled in a "self-centered" manner. In this paper, we propose a "human-centered" modeling scheme for collaborative agents that aims to enhance the experience of humans. Specifically, we model the experience of humans as the goals they expect to achieve during the task. We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e.g., winning games). To achieve this, we propose the Reinforcement Learning from Human Gain (RLHG) approach. The RLHG approach introduces a "baseline", which corresponds to the extent to which humans primitively achieve their goals, and encourages agents to learn behaviors that can effectively enhance humans in achieving their goals better. We evaluate the RLHG agent in the popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings, by conducting real-world human-agent tests. Both objective performance and subjective preference results show that the RLHG agent provides participants better gaming experience. | [] | [] | Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain | [
"Yiming Gao",
"Feiyu Liu",
"Liang Wang",
"Dehua Zheng",
"Zhenjie Lian",
"Weixuan Wang",
"Wenjin Yang",
"Siqin Li",
"Xianliang Wang",
"Wenhui Chen",
"Jing Dai",
"QIANG FU",
"Yang Wei",
"Lanxiao Huang",
"Wei Liu"
] | 2401.16444 | 19,199 | https://openreview.net/forum?id=BqEvdOS1Hs |
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[] | Poster | [] | A Marked Temporal Point Process (MTPP) is a stochastic process whose realization is a set of event-time data. MTPP is often used to understand complex dynamics of asynchronous temporal events such as money transaction, social media, healthcare, etc. Recent studies have utilized deep neural networks to capture complex temporal dependencies of events and generate embedding that aptly represent the observed events. While most previous studies focus on the inter-event dependencies and their representations, how individual events influence the overall dynamics over time has been under-explored. In this regime, we propose a Decoupled MTPP framework that disentangles characterization of a stochastic process into a set of evolving influences from different events. Our approach employs Neural Ordinary Differential Equations (Neural ODEs) to learn flexible continuous dynamics of these influences while simultaneously addressing multiple inference problems, such as density estimation and survival rate computation. We emphasize the significance of disentangling the influences by comparing our framework with state-of-the-art methods on real-life datasets, and provide analysis on the model behavior for potential applications. | [] | [] | Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations | [
"Yujee Song",
"Donghyun LEE",
"Rui Meng",
"Won Hwa Kim"
] | 19,193 | https://openreview.net/forum?id=BuFNoKBiMs |
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[] | Poster | [] | Generative modeling via diffusion-based models has been achieving state-of-the-art results on various generation tasks. Most existing diffusion models, however, are limited to a single-generation modeling. Can we generalize diffusion models with the ability of multi-task generative training for more generalizable modeling? In this paper, we propose a principled way to define a diffusion model for this purpose by constructing a unified multi-task diffusion model in a common {\em diffusion space}. We define the forward diffusion process to be driven by an information aggregation from multiple types of task-data, {\it e.g.}, images for a generation task and labels for a classification task. In the reverse process, we enforce information sharing by parameterizing a shared backbone denoising network with additional task-specific decoder heads. Such a structure can simultaneously learn to generate different types of multi-task data with a multi-task loss, which is derived from a multi-task variational lower bound that generalizes the standard diffusion model. We propose several multi-task generation settings to verify our framework, including image transition, masked-image training, joint image-label and joint image-representation generative modeling. Extensive experimental results on ImageNet indicate the effectiveness of our framework for various multi-task generative modeling, which we believe is an important research direction worthy of more future explorations. | [] | [] | Diffusion Models for Multi-Task Generative Modeling | [
"Changyou Chen",
"Han Ding",
"Bunyamin Sisman",
"Yi Xu",
"Ouye Xie",
"Benjamin Z. Yao",
"Son Dinh Tran",
"Belinda Zeng"
] | 18,289 | https://openreview.net/forum?id=cbv0sBIZh9 |
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[] | Poster | [] | Updating truncated Singular Value Decomposition (SVD) has extensive applications in representation learning.The continuous evolution of massive-scaled data matrices in practical scenarios highlights the importance of aligning SVD-based models with fast-paced updates.Recent methods for updating truncated SVD can be recognized as Rayleigh-Ritz projection procedures where their projection matrices are augmented based on the original singular vectors.However, the updating process in these methods densifies the update matrix and applies the projection to all singular vectors, resulting in inefficiency.This paper presents a novel method for dynamically approximating the truncated SVD of a sparse and temporally evolving matrix.The proposed method takes advantage of sparsity in the orthogonalization process of the augment matrices and employs an extended decomposition to store projections in the column space of singular vectors independently.Numerical experimental results on updating truncated SVD for evolving sparse matrices show an order of magnitude improvement in the efficiency of our proposed method while maintaining precision comparing to previous methods. | [] | [] | Fast Updating Truncated SVD for Representation Learning with Sparse Matrices | [
"Haoran Deng",
"Yang Yang",
"Jiahe Li",
"Cheng Chen",
"Weihao Jiang",
"Shiliang Pu"
] | 2401.09703 | 19,171 | https://openreview.net/forum?id=CX2RgsS29V |
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[] | Spotlight Poster | [
"https://github.com/kaistAI/FLASK"
] | Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction. However, previous studies have mainly focused on coarse-grained evaluation (i.e. overall preference-based evaluation), which limits interpretability since it does not consider the nature of user instructions that require instance-wise skill composition. In this paper, we introduce FLASK (Fine-grained Language Model Evaluation based on Alignment Skill Sets), a fine-grained evaluation protocol for both human-based and model-based evaluation which decomposes coarse-level scoring to a skill set-level scoring for each instruction. We experimentally observe that the fine-graininess of evaluation is crucial for attaining a holistic view of model performance and increasing the reliability of the evaluation. Using FLASK, we compare multiple open-source and proprietary LLMs and observe a high correlation between model-based and human-based evaluations. | [] | [] | FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets | [
"Seonghyeon Ye",
"Doyoung Kim",
"Sungdong Kim",
"Hyeonbin Hwang",
"Seungone Kim",
"Yongrae Jo",
"James Thorne",
"Juho Kim",
"Minjoon Seo"
] | 2307.10928 | 19,170 | https://openreview.net/forum?id=CYmF38ysDa |
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[] | Poster | [] | Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Both automatic and human evaluations consistently indicate that WizardLM outperforms baselines such as Alpaca (trained from Self-Instruct) and Vicuna (trained from human-created instructions). The experimental results demonstrate that the quality of instruction-following dataset crafted by Evol-Instruct can significantly improve the performance of LLMs. | [] | [] | WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions | [
"Can Xu",
"Qingfeng Sun",
"Kai Zheng",
"Xiubo Geng",
"Pu Zhao",
"Jiazhan Feng",
"Chongyang Tao",
"Qingwei Lin",
"Daxin Jiang"
] | 19,164 | https://openreview.net/forum?id=CfXh93NDgH |
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[] | Poster | [] | Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing MARL algorithms are model-free, limiting sample efficiency and hindering their applicability in more challenging scenarios. In contrast, model-based reinforcement learning (MBRL), particularly algorithms integrating planning, such as MuZero, has demonstrated superhuman performance with limited data in many tasks. Hence, we aim to boost the sample efficiency of MARL by adopting model-based approaches. However, incorporating planning and search methods into multi-agent systems poses significant challenges. The expansive action space of multi-agent systems often necessitates leveraging the nearly-independent property of agents to accelerate learning. To tackle this issue, we propose the MAZero algorithm, which combines a centralized model with Monte Carlo Tree Search (MCTS) for policy search. We design an ingenious network structure to facilitate distributed execution and parameter sharing. To enhance search efficiency in deterministic environments with sizable action spaces, we introduce two novel techniques: Optimistic Search Lambda (OS($\lambda$)) and Advantage-Weighted Policy Optimization (AWPO). Extensive experiments on the SMAC benchmark demonstrate that MAZero outperforms model-free approaches in terms of sample efficiency and provides comparable or better performance than existing model-based methods in terms of both sample and computational efficiency. | [] | [] | Efficient Multi-agent Reinforcement Learning by Planning | [
"Qihan Liu",
"Jianing Ye",
"Xiaoteng Ma",
"Jun Yang",
"Bin Liang",
"Chongjie Zhang"
] | 19,160 | https://openreview.net/forum?id=CpnKq3UJwp |
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[] | Spotlight Poster | [
"https://github.com/tongdaxu/Idempotence-and-Perceptual-Image-Compression"
] | Idempotence is the stability of image codec to re-compression. At the first glance, it is unrelated to perceptual image compression. However, we find that theoretically: 1) Conditional generative model-based perceptual codec satisfies idempotence; 2) Unconditional generative model with idempotence constraint is equivalent to conditional generative codec. Based on this newfound equivalence, we propose a new paradigm of perceptual image codec by inverting unconditional generative model with idempotence constraints. Our codec is theoretically equivalent to conditional generative codec, and it does not require training new models. Instead, it only requires a pre-trained mean-square-error codec and unconditional generative model. Empirically, we show that our proposed approach outperforms state-of-the-art methods such as HiFiC and ILLM, in terms of Fréchet Inception Distance (FID). The source code is provided in https://github.com/tongdaxu/Idempotence-and-Perceptual-Image-Compression. | [] | [] | Idempotence and Perceptual Image Compression | [
"Tongda Xu",
"Ziran Zhu",
"Dailan He",
"Yanghao Li",
"Lina Guo",
"Yuanyuan Wang",
"Zhe Wang",
"Hongwei Qin",
"Yan Wang",
"Jingjing Liu",
"Ya-Qin Zhang"
] | 2401.08920 | 19,156 | https://openreview.net/forum?id=Cy5v64DqEF |
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[] | Poster | [] | Though some research efforts have been dedicated to constrained Bayesian optimization (BO), there remains a notable absence of a principled approach with a theoretical performance guarantee in the decoupled setting. Such a setting involves independent evaluations of the objective function and constraints at different inputs, and is hence a relaxation of the commonly-studied coupled setting where functions must be evaluated together. As a result, the decoupled setting requires an adaptive selection between evaluating either the objective function or a constraint, in addition to selecting an input (in the coupled setting). This paper presents a novel constrained BO algorithm with a provable performance guarantee that can address the above relaxed setting. Specifically, it considers the fundamental trade-off between exploration and exploitation in constrained BO, and, interestingly, affords a noteworthy connection to active learning. The performance of our proposed algorithms is also empirically evaluated using several synthetic and real-world optimization problems. | [] | [] | Optimistic Bayesian Optimization with Unknown Constraints | [
"Quoc Phong Nguyen",
"Wan Theng Ruth Chew",
"Le Song",
"Bryan Kian Hsiang Low",
"Patrick Jaillet"
] | 19,154 | https://openreview.net/forum?id=D4NJFfrqoq |
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[] | Poster | [] | Despite the dominance and effectiveness of scaling, resulting in large networks with hundreds of billions of parameters, the necessity to train overparametrized models remains poorly understood, while training costs grow exponentially. In this paper, we explore parameter-efficient training techniques as an approach to training large neural networks. We introduce a novel method called ReLoRA, which utilizes low-rank updates to train high-rank networks. We apply ReLoRA to training transformer language models with up to 1.3B parameters and demonstrate comparable performance to regular neural network training. ReLoRA saves up to 5.5Gb of RAM per GPU and improves training speed by 9-40% depending on the model size and hardware setup. Our findings show the potential of parameter-efficient techniques for large-scale pre-training. | [] | [] | ReLoRA: High-Rank Training Through Low-Rank Updates | [
"Vladislav Lialin",
"Sherin Muckatira",
"Namrata Shivagunde",
"Anna Rumshisky"
] | 2307.05695 | 19,143 | https://openreview.net/forum?id=DLJznSp6X3 |
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[] | Poster | [] | The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed a serious concern to online safety and public trust. A fundamental research question is: will LLM-generated misinformation cause more harm than human-written misinformation? We propose to tackle this question from the perspective of detection difficulty. We first build a taxonomy of LLM-generated misinformation. Then we categorize and validate the potential real-world methods for generating misinformation with LLMs. Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm. We also discuss the implications of our discovery on combating misinformation in the age of LLMs and the countermeasures. | [] | [] | Can LLM-Generated Misinformation Be Detected? | [
"Canyu Chen",
"Kai Shu"
] | 2309.13788 | 18,287 | https://openreview.net/forum?id=ccxD4mtkTU |
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[] | Poster | [] | Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robust architectures, especially when adversarial training is considered. In this work, we aim to address these two challenges, making twofold contributions. First, we release a comprehensive data set that encompasses both clean accuracy and robust accuracy for a vast array of adversarially trained networks from the NAS-Bench-201 search space on image datasets. Then, leveraging the neural tangent kernel (NTK) tool from deep learning theory, we establish a generalization theory for searching architecture in terms of clean accuracy and robust accuracy under multi-objective adversarial training. We firmly believe that our benchmark and theoretical insights will significantly benefit the NAS community through reliable reproducibility, efficient assessment, and theoretical foundation, particularly in the pursuit of robust architectures. | [] | [] | Robust NAS under adversarial training: benchmark, theory, and beyond | [
"Yongtao Wu",
"Fanghui Liu",
"Carl-Johann Simon-Gabriel",
"Grigorios Chrysos",
"Volkan Cevher"
] | 2403.13134 | 18,286 | https://openreview.net/forum?id=cdUpf6t6LZ |
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[] | Poster | [] | Congestion is a common failure mode of markets, where consumers compete inefficiently on the same subset of goods (e.g., chasing the same small set of properties on a vacation rental platform). The typical economic story is that prices decongest by balancing supply and demand. But in modern online marketplaces, prices are typically set in a decentralized way by sellers, and the information about items is inevitably partial. The power of a platform is limited to controlling *representations*---the subset of information about items presented by default to users. This motivates the present study of *decongestion by representation*, where a platform seeks to learn representations that reduce congestion and thus improve social welfare. The technical challenge is twofold: relying only on revealed preferences from the choices of consumers, rather than true preferences; and the combinatorial problem associated with representations that determine the features to reveal in the default view. We tackle both challenges by proposing a *differentiable proxy of welfare* that can be trained end-to-end on consumer choice data. We develop sufficient conditions for when decongestion promotes welfare, and present the results of extensive experiments on both synthetic and real data that demonstrate the utility of our approach. | [] | [] | Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces | [
"Omer Nahum",
"Gali Noti",
"David C. Parkes",
"Nir Rosenfeld"
] | 2306.10606 | 18,283 | https://openreview.net/forum?id=coIaBY8EVF |
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[] | Spotlight Poster | [] | We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in clinical research, enabling population-level analysis of morphological variation in bones and organs. Traditional methods of SSM construction have limitations, including the requirement of noise-free surface meshes or binary volumes, reliance on assumptions or templates, and prolonged inference times due to simultaneous optimization of the entire cohort. Point2SSM overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. While deep learning on 3D point clouds has seen success in unsupervised representation learning and shape correspondence, its application to anatomical SSM construction is largely unexplored. We conduct a benchmark of state-of-the-art point cloud deep networks on the SSM task, revealing their limited robustness to clinical challenges such as noisy, sparse, or incomplete input and limited training data. Point2SSM addresses these issues through an attention-based module, providing effective correspondence mappings from learned point features. Our results demonstrate that the proposed method significantly outperforms existing networks in terms of accurate surface sampling and correspondence, better capturing population-level statistics. The source code is provided at https://github.com/jadie1/Point2SSM. | [] | [] | Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds | [
"Jadie Adams",
"Shireen Elhabian"
] | 2305.14486 | 19,123 | https://openreview.net/forum?id=DqziS8DG4M |
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[] | Poster | [] | Most recent work in goal oriented visual navigation resorts to large-scale machine learning in simulated environments. The main challenge lies in learning compact representations generalizable to unseen environments and in learning high-capacity perception modules capable of reasoning on high-dimensional input. The latter is particularly difficult when the goal is not given as a category ("ObjectNav") but as an exemplar image ("ImageNav"), as the perception module needs to learn a comparison strategy requiring to solve an underlying visual correspondence problem. This has been shown to be difficult from reward alone or with standard auxiliary tasks. We address this problem through a sequence of two pretext tasks, which serve as a prior for what we argue is one of the main bottleneck in perception, extremely wide-baseline relative pose estimation and visibility prediction in complex scenes. The first pretext task, cross-view completion is a proxy for the underlying visual correspondence problem, while the second task addresses goal detection and finding directly. We propose a new dual encoder with a large-capacity binocular ViT model and show that correspondence solutions naturally emerge from the training signals. Experiments show significant improvements and SOTA performance on the two benchmarks, ImageNav and the Instance-ImageNav variant, where camera intrinsics and height differ between observation and goal. | [] | [] | End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon | [
"Guillaume Bono",
"Leonid Antsfeld",
"Boris Chidlovskii",
"Philippe Weinzaepfel",
"Christian Wolf"
] | 18,282 | https://openreview.net/forum?id=cphhnHjCvC |
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